Yolo ship detection Our optimized ship detector is based on a hybrid data-model centric approach, which utilizes the statistical characteristics of the datasets under observation and has an efficient model architecture. Synthetic Aperture Radar (SAR) image detection tasks play a vital role in ship detection, which has consistently been a research In tasks that require ship detection and recognition, the irregular shapes of ships and complex backgrounds pose significant challenges. The application of ship detection for assistant In this paper, we propose a new infrared ship target detection algorithm, YOLO-IRS (YOLO for infrared ship target), based on YOLOv10, which improves detection accuracy while maintaining detection To address these challenges, this paper introduces an enhanced SAR ship detection model, termed ADV-YOLO, which builds upon the YOLOv8 framework. YOLO detection model is a lightweight and efficient target detection algorithm. CNN-based SAR ship detectors are challenged to meet real-time requirements because of a large number of parameters. Synthetic Aperture Radar (SAR) is extensively used for vessel detection due to its In the task of ship target detection, due to the complex image background and more irrelevant interference, the difficulty of ship target detection is increased. First, we discuss the introducti on of lar ge size convolution ke rnel in the residual module of the Infrared-based detection of small targets on ships is crucial for ensuring navigation safety and effective maritime traffic management. First, we devised Elaborating on the YOLO framework, the research by (T. When compared with methods to process daytime RGB photos, processing infrared images has challenges due to the reduced signal-to-clutter ratio (SCR), indistinct outlines, and inadequate spatial resolutions. Self-attention is an attentional process that connects several points in Ship detection is vital for maritime safety and vessel monitoring, but challenges like false and missed detections persist, particularly in complex backgrounds, multiple scales, and adverse weather conditions. FE-YOLO: YOLO ship detection algorithm based on feature fusion and feature enhancement Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once The technology for detecting maritime targets is crucial for realizing ship intelligence. In the neck of YOLOv8n, we replaced ordinary convolution (Conv) with a lighter ghost convolution (GhostConv) and introduced reparameterized ghost (RepGhost) To address this issue, this paper introduces a novel detection model-ship detection YOLO (SD-YOLO), which improves small object detection accuracy while maintaining real-time performance. Zhang & Zhang, Citation 2019) introduces an innovative technique for rapid ship detection in SAR images, utilising a grid-based CNN approach (G-CNN). However, the complexity of VGG-16 makes it very time-consuming and In order to improve the ship detection accuracy and real-time performance, this paper proposed a ship detection algorithm based on YOLO V5, in which the feature extraction process was merged with the GhostbottleNet algorithm. Specifically, the ship detection module is designed to obtain the positions of ships in a single-frame image. Firstly, we introduce the The results confirm that, compared with YOLOv8n, the proposed SHIP-YOLO on SAR Ship Detection dataset (SSDD) reduces the parameters and floating-point operations (FLOPs) by 17% and 11% Much of the work has proven to be effective in the field of marine object detection. Ship detection algorithms based on the CNN have achieved good results, albeit with many missed and false detections. This paper mainly compared several mainstream YOLO series, focusing on detection accuracy and efficiency in SAR ship detection. Ship detection plays a pivotal role in efficient marine monitoring, port management, and safe navigation. Firstly, the backbone network adopts MobileNetv3, which uses lightweight deep separable convolution Download Citation | FE-YOLO: YOLO ship detection algorithm based on feature fusion and feature enhancement | The technology for detecting maritime targets is crucial for realizing ship intelligence. However, existing ship detection algorithms are ineffective in detecting multi-scale ships in complex scenes. Now, since we have got only one object In light of the characteristics of ship targets with large-scale changes and harrowing feature extraction in the optical remote sensing images, we propose an improved ship detection algorithm for optical remote sensing based on YOLOv8. Therefore, if YOLO framework is directly applied to ship detection in SAR images, there will be a lot of problems such as low accuracy, false alarms, missed detection and poor fine-grained classification performance. Their methodology amplifies the detection rate by segmenting the input image and incorporating depth-wise separable convolution. Abstract Ship detection is a crucial task for waterway surveillance and channel optimization, especially in close proximity to the shore. Green bounding box represents correctly-detected ships, red bounding box indicates missing ships, and Traditional visible light target detection is usually applied in scenes with good visibility, while the advantage of infrared target detection is that it can detect targets at nighttime and in harsh weather, thus being able to be applied to ship detection in complex sea conditions all day long. Our improved Glimmer YOLO model has a new small target detection layer and an attention mechanism, which focuses on enhancing the detection of small targets. Firstly, we design a feature XU et al. Ship image recognition by synthetic aperture radar (SAR) is a crucial technology for intelligent shipping and maritime safety monitoring. Qian Huang 1, 2 Huashan Sun 1, 2 Yiming W ang 1, 2 Y ang Yuan 1, 2. In response to these challenges, this research presents YOLO-MSD, a Ship detection aims to recognize ships and their course borders from an image. 2. Synthetic aperture radar (SAR) is widely used for ship target detection with the application of deep learning techniques. The used YOLO network presents efficient detection for the ship body; the fusion framework and the usage of wakes are the main focus in this paper. Current ship detection algorithms mainly rely on radar remote sensing images, which cannot achieve accurate and real-time ship detection. There are many attempts on solving The complete architecture of our ship detection algorithm is built based on the YOLO framework introduced by Redmon et al. This paper proposes Recently, synthetic aperture radar (SAR) images ship target detection for marine security plays a vital role. Addressing these challenges, this study presents Light-YOLO, When the YOLO architecture is used for ship detection, it mainly has the following two disadvantages: (1) It has poor recognition where small target objects are concerned, and the positioning is inaccurate. The majority of modern algorithms can achieve successful ship detection outcomes when working with multiple-scale ships on a large sea surface. Expand To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). data/ship. cfg. The contemporary research regarding infrared ship imagery is insufficient and remains in need of addressing the issues related to smaller object sizes and more elaborate information. as can be observed in the figures, whether it is a Synthetic aperture radar is widely applied to ship detection due to generating high-resolution images under diverse weather conditions and its penetration capabilities, making SAR images a valuable data source. Therefore, we designed a lightweight and efficient Considering the urgent need for real-time target detection in unmanned ship autonomous navigation, this paper chooses to study the single-stage target detection network YOLO. To address this problem, we propose a novel small ship detection In addition, Y. However, the model reached a relatively lower score in terms of F1 score in comparison with YOLO-v8 and ship-fire-net model Based on the YOLOv8n model, this paper proposes an improved lightweight ship detection model YOLO-FE. 0%, outperforming recent models such as FoveaBox (+4. Two-stage target detection algorithms, such as faster R-CNN[] and mask R-CNN[], are well-known within the field. Since traditional image processing-based methods are not robust, deep learning-based image recognition methods can automatically obtain the features of small ships. In addition, thermal infrared ship datasets were made using the SDGSAT-1 thermal Lightweight ship detection offers the dual benefits of rapid detection and low computational cost, making it particularly advantageous for inland waterway safety monitoring. The present project was conducted as part of my diploma thesis which focuses on the investigation of methods for the effective detection of ships in synthetic aperture radar satellite imagery utilizing deep learning techniques. Although existing research has made great progress in improving the model detection accuracy, it often comes at the cost of FE-YOLO: YOLO ship detection algorithm based on feature fusion and feature enhancement. The YOLO series models continue to apply the most recent research findings for continual iterative optimization due to the ongoing evolution of the computer vision field. However, most recent work has focused on adapting the YOLO framework for specific ship Darknet requires certain files to know how and what to train. For realizing real-time accurate recognition of visible ship targets, an improved ship target detection method YOLO-Ship derived from YOLOv5 is advanced. Therefore, a visible image-based ship detection model is proposed that employs a multi-scale weighted feature fusion struc-ture with the YOLOv4 detection model to improve the efficacy of small ship detection. The next sections will guide you through detecting objects with the YOLO system using a pre-trained model. Firstly, the BottleBlock of C2f module is replaced by FasterN et Block in the FasterNet module to reduce the complexity of the model and improve the calculation speed of the model. Default: . Abstract. First, we discuss the introduction of large size convolution kernel in the residual YOLO adopted an end-to-end training and detection method that balances speed and accuracy, making it an effective way for synthetic aperture radar (SAR) ship detection. Many scholars have proposed beneficial improvement schemes for using YOLO framework to detect ship in SAR images. For the multi-scale problem of SAR ship targets in complex scenes, we proposed an improved YOLOv5 detection method using Convolutional An improved YOLO v2 algorithm for small ship detection. The ship detection method proposed by Leng et al. We combine To address the data scarcity in small-scale ship detection, bridge the gap between small-scale ship detection and general object detection, and mitigate the impact of small objects on maritime safety, we collect a multi-scale dataset with a particular emphasis on detecting small objects on the ocean surface, named the iShip-1. names file contains the name of the object categories you want to detect. e. We note that our method can be regarded as an additional step after the conventional Airbus Ship Detection challenge with YOLOv4 object detector. proposes an improved ship target detection network, CA-YOLO v3 Network, to address the challenge of identifying ship categories and positions in complex sea environments. Experimental results show that the algorithm can accurately detect and identify targets such as ships, buoys and speedboats. With a C3REGhost module designed for the backbone network, our method achieves an improved ability to extract ship features. To be specific, to improve the detection ability of YOLYv5 for multi-scale ship targets, the feature refinement module is designed. Furthermore, the In this paper, a feature-enhanced lightweight ship object detection method called YOLO-Ships is proposed. The ubiquitous interference of cloud and fog led to missed detection and false-alarms when using imagery-based optical satellite remote sensing. major improvements were incorporated in the LH-YOLO. An off-shore ship detection method with scene classification and a saliency-tuned Synthetic Aperture Radar (SAR) imaging technology is crucial for maritime vessel monitoring. The annotations are in Yolo format, i. Ships are important targets for modern naval warfare detection and reconnaissance. In recent years, deep neural networks (DNNs) have been frequently used for this purpose. However, traditional detection algorithms are not ideal due to the diversity of marine targets and complex background environments. This study introduces YOLO-GCV, a lightweight ship detection algorithm based on YOLOv7-tiny. However, in coastal areas where the density of ships is high and there is a In this paper, we introduce YOLO-FCNET, a novel ship detection approach that leverages the frequency domain analysis based on YOLOv8, to enhance both the precision and efficiency of SAR ship detection. By optimizing this network, we aim to maintain high accuracy while meeting the real-time and rapid decision-making requirements of unmanned ships, thereby making An improved YOLO-V4 detection model (ShipYOLO) is used to detect ships and a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling is designed, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. The new backbone network On Ship7000 dataset, YOLO-Ship detection method has a more outstanding accuracy than YOLOv5 and YOLOv3 algorithms, and its average detection accuracy under different IoU In response to the challenges faced in ship detection under complex marine environments, this study proposed a novel ship detection algorithm, termed CSD-YOLO, which addresses the limitations of maintaining detection Lightweight ship detection offers the dual benefits of rapid detection and low computational cost, making it particularly advantageous for inland waterway safety monitoring. On the other hand, most detection methods struggle to accurately identify ships that are small in size. 9% higher than that of YOLOv7, Therefore, if YOLO framework is directly applied to ship detection in SAR images, there will be a lot of problems such as low accuracy, false alarms, missed detection and poor fine-grained classification performance. The N-YOLO includes a noise level classifier (NLC), a SAR target As mentioned, we combine ship body detection and the information of ship wakes to achieve ship detection. If you need COCO format, Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. Ship detection over long distances is crucial for the visual perception of intelligent ships. In this paper, we propose a lightweight, single-stage SAR ship target EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models. First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the Automatic ship detection in SAR images plays an essential role in both military and civilian fields. Based on the YOLOv8n algorithm, It is challenging to extract effective features from multi-scale ship targets, leading to high missing alarm rate. In this article, we designed a large kernel convolutional YOLO (Lk-YOLO) detection model based on Anchor free for one-stage ship detection. However, applying ship detection on drones brings two main difficulties. Parameters: file (file): The image or video file to be uploaded. However, due to the limited pixels of ships over long distances, accurate features of such ships are difficult to At present, ship detectors have many problems, such as too many hyperparameter, poor recognition accuracy and imprecise regression boundary. The algorithm achieves object detection through training a single neural network model and performing single-pass forward propagation, offering high real-time performance, global feature extraction, multi-scale detection In order to address the issues of low accuracy, limited perception of small targets, and the lack of cross-scale detection capabilities in generic object detection algorithms for ship detection, we have created a self-constructed, diverse dataset called MixShips. 0 datasets is 2. However, most of the existing deep learning detection methods introduce complex models and huge calculations while improving the detection accuracy, which is not conducive to the application of real-time ship detection. To address these complexities, in this paper, the MSFA-YOLO algorithm, a novel multiscale SAR ship detection approach em-powered by a fused attention mechanism, is presented. Normally, top state-of-art CNN for object detection as Faster R-CNN failed in order to detect small objects, as is tested in []. Firstly, we For the detection of relatively small objects, the NWD (normalized Wasserstein distance) is used to optimize the IOU value. In this paper, the K Synthetic aperture radar (SAR) is a technique widely used in the field of ship detection. All experiments were performed on a portable computer with Ship detection based on wide-area remote sensing imagery has a wide range of applications in areas such as ship supervision and rescue at sea. It achieved a The results confirm that, compared with YOLOv8n, the proposed SHIP-YOLO on SAR Ship Detection dataset (SSDD) reduces the parameters and floating-point operations (FLOPs) by 17% and 11%, respectively, and improves the precision, recall, and mean average precision (mAP) by 1. The single-stage YOLO target detection algorithm based on full convolution, is widely used for target detection, tracking, and segmentation Han et al. , 2016) is used for classification and bounding box regression. Moreover, this quantization scheme has good adaptability to various scenes. Compared with optical images, ships in SAR images contain less feature information. To evaluate the impact of each module and improvement component, this study conducted an ablation experiment. To improve the precision without reducing the speed, a target detection method SD-YOLO for SAR ship detection is proposed. Firstly, from feature extraction, we propose a multi-scale grouped feature extraction module called C2f-MSG, which mitigates the computational At present, deep learning has been widely used in SAR ship target detection, but the accurate and real-time detection of multi-scale targets still faces tough challenges. To address the loss of With the continuous development of marine economy, marine ship detection has a very wide application prospect. B. This problem arises from the following factors: 1) the current 8-b rescaling schemes make the images lose some important information about ships in low-resolution imagery; 2) the effective In this paper, a complete YOLO-based ship detection method (CYSDM) for TIRSIs under complex backgrounds is proposed. First, a newly designed StarNet- Ship detection technology plays a major role in maritime traffic safety and waterway management. For improved results, future work could include experimentation with larger models, and augmentation strategies could be explored to help balance out In summary, the LAM-YOLO ship detection algorithm proposed in this paper outperforms the three lightweight algorithms and the advanced YOLOv6 and YOLOv7 in terms of overall performance, improves the misdetection and omission detection phenomena in both far-sea and near-shore scenarios, and achieves a good trade-off between detection accuracy YOLOv5's architecture consists of three main parts: Backbone: This is the main body of the network. To address these issues, a high-precision ship target detection method named DBW-YOLO, which builds upon YOLOv7 LMO-YOLO: A Ship Detection Model for Low-Resolution Optical Satellite Imagery. This notebook aims to provide a step-by-step guide on training a YOLOv8 model for ship detection. It also reduces the number of required model parameters, optimizes the SPPF module and introduces a BoTNet module A novel detection model-ship detection YOLO (SD-YOLO), which improves small object detection accuracy while maintaining real-time performance and redesigns YOLOv5's neck layer using a Bi-directional Feature Pyramid Network (BiFPN) to optimize multi-scale feature fusion. Three. With relatively little time and effort I trained a YOLOv8 model for ship detection. Therefore, we choose We evaluated YOLOv3 (YOLO version 3) and YOLT in order to detect small ship on optical satellite imagery, by small, it is referred to small objects in the image. Moreover, the complexity of backgrounds in remote sensing images of ships and the clustering of vessels also adversely affect the accuracy of ship detection. FE-YOLO, based on the YOLOv7 model, We propose a visible image-based ship detection model that employs a multi- scale weighted feature fusion structure with the YOLOv4 This paper proposes an enhanced model based on YOLO-V4 for ship detection. We propose the feature fusion and feature enhancement YOLO (FE-YOLO) algorithm for better ship detection performance. We also carry out a The Hybrid YOLO model has been developed for ship detection from open-source SAR images 19. This integral dataset is composed of 39,729 ship chips cropped from 102 Chinese Gaofen-3 images and 108 Sentinel-1 with 256 by 256 pixels. Researchers have explored various methods to fully exploit the all-weather characteristics of Synthetic aperture radar (SAR) images to achieve high-precision, real-time, computationally efficient, and easily deployable ship Abstract: In response to the challenges posed by small objects, high noise, and complex backgrounds in synthetic aperture radar (SAR) ship detection, we proposed a lightweight model called SHIP-YOLO. A deep convolutional neural network is used to achieve ship body detection, and a feature-based processing To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). Transformers are self-attention-based approaches that have demonstrated excellent performance in vision tasks, such as semantic segmentation and image categorization. Xiaotong Guo 1, 2 Qiang Gao 3. In addition, the detection of small targets Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. This algorithm Based on a statistical analysis of existing articles on ship detection, it is clear that most ship detection methods primarily rely on the YOLO series. , [class, x_center, y_center, width, height]. 1 Key Laboratory of Water Big Data T Based on the characteristics of infrared ocean ship scenes, we propose a CAA-YOLO infrared ship detection algorithm based on one-stage detector YOLOv5. Therefore, we propose an FS-YOLO for detecting ship targets in complex backgrounds. This paper proposes an IL-YOLOv5 ship detection method that combines the YOLOv5 network model and incremental learning method. The detection of such small targets in the expansive and dynamic marine environment is notably challenging, leading to low detection rates. We have also proposed an improved algorithm called YOLOv7_OCM, which builds upon YOLOv7. In YOLO-MSSD, a new Spatial and Channel Attention Module (SCAM) is designed during the stages of feature extraction and fusion. /Model/Boat-detect-medium. However, the inherent characteristics of SAR images, such as limited feature resolution and speckle noise, pose a series of challenges for ship detection. This research paper presents an innovative ship detection method that integrates the YOLOv7 (You Only Look Once) object detection algorithm with the Jetson Nano platform. In response to the challenges posed by small objects, Accurate ship detection is essential for a wide range of maritime applications. . (2) It lacks the ability to obtain global information on the image that can benefit the network in terms of accuracy and efficiency. names; cfg/ship. A lightweight visual transformer, MobileViTSF, is proposed and combined with the YOLOv8 model. However, most current deep learning-based algorithms tend to increase network depth to improve detection accuracy, which may result in the loss of effective features of the target. And YOLO detection head (Redmon et al. 1 SAR ship detection method for YOLO series. Zhang and Zhang (Citation 2019) proposed a new method for high-speed ship detection in SAR images based on G-CNN, drawing on the idea of YOLO. In light of this circumstance, we propose an SAR ship detection network, YOLO-range compressed (YOLO-RC), which utilizes amplitude gradient and geometric scale characteristics in the range-compressed domain for improved performance. Inspired by the YOLOv8n algorithm, we introduce a novel neural network architecture called YOLO-SHIP-DETECTION (YOLO-SD). Moreover, the inherent high noise levels and low contrast of an SAR ship detection model named LH-YOLO, which is based on the YOLOv8n. Ship detection based on YOLO algorithm for visible images. The YOLO-ship model replaces the convolutional layer in YOLOv5 with MinxConv and inserts the CA attention module, which results in an mAP up to 72. (Citation 2021) trained the YOLOv5 model on the dataset, demonstrating that this method has a very wide application prospect in ship detection. Among the solutions to this problem, the YOLO has received more and more attention due to its advantages such as high speed. By adjusting the detection bounding boxes, these models achieve rapid predictions of object positions and categories. A lightweight and efficient ship object detection algorithm YOLO-MBS based on improved Yolov5 is proposed. Recently, deep learning techniques have been extensively used to detect ships in synthetic aperture radar (SAR) images. As a result, the traditional ship detection method is difficult to achieve the ideal detection effect. To solve this problem, an efficient lightweight The previously proposed methods often fail to effectively incorporate the characteristics of range-compressed domain. As a state-of-the-art detector, YOLOv5 has the advantages of fast convergence, high accuracy and lightweight model. Our method is developed using both ship body detection and ship wake detection and combines deep learning and feature-based image processing. Chen et al. However, in certain complex environments, such as near shore or with small ships, the problem of false alarms and missed detections still exists. January 2022; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15:4117-4131; Thanks to incredible work of YOLOv7 (YOLOv7 paper) for object detection, this repo applies YOLOv7 model to implement ship detection from SAR images. [18] proposed Yolo-Ship based on YOLOv4, utilizing reconstructed convolution techniques and residual connections, significantly increasing small target detection accuracy. While Official-SSDD was used in model 2, and a combination of ADV-YOLO: improved SAR ship detection model based on YOLOv8 Page 3 of 32 34 from noise or ocean backgrounds. The proposed methodology combines the power of YOLOv7’s real-time and accurate ship detection capabilities with the efficient We trained the YOLO-V4-based ship detection model on SSDD while introducing YOLO-V4-tiny , a simplified version of YOLO-V4, as a comparison. Secondly, the EMA attention mechanism is introduced to further Wang et al. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly YOLO detection model is a lightweight and efficient target detection algorithm. We begin by incorporating the FCSE Block, a module that enriches the model's feature representation by intelligently amplifying salient features YOLO [24] treats target detection as a regression problem, dividing the image into an S × S grid and predicting the detection frame information and class probability of the object within each grid. Firstly, before data training, the methods of image blending and small Due to the significant discrepancies in the distribution of ships in nearshore and offshore areas, the wide range of their size, and the randomness of target orientation in the sea, traditional detection models in the field of computer vision struggle to achieve performance in SAR image ship target detection comparable to that in optical image detection. To address these issues, this paper proposes a lightweight ship object detection model called YOLOv7-Ship to perform end-to-end ship detection in complex marine environments. With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain detection speed should be ensured while improving accuracy; (2) The diversity of ships It has been observed that the existing convolutional neural network (CNN)-based ship detection models often result in high false detection rate in low-resolution optical satellite images. Our algorithm first corrects the noisy labels of the original dataset due to misclassification and constructs an infrared ship dataset (ISD) containing different concentrations of haze through an Visible ship detection results of state-of-the-art detectors and YOLO series on complex detection scenes from the test of HRSID. Therefore, this paper proposes an optimized model named SSMA-YOLO, based on YOLOv8n. Further, the noise level classifier has been appended to distinguish images with noise levels Synthetic aperture radar (SAR) enables precise object localization and imaging, which has propelled the rapid development of algorithms for maritime ship identification and detection. 6% and 3. The proposed algorithm strikes an effective balance between detection accuracy and speed. Faster R-CNN achieves high accuracy by initially generating candidate regions, followed by classifying In this study, we propose a practical and efficient scheme for ship detection in remote sensing imagery. Introduction Ship detection is of great significance for marine automatic fishery management, port rescue, marine traffic maintenance, Infrared object detection constitutes a significant ship-targeting methodology, exerting a vital role in maritime safety. At first, we insert the improved “coordinate Automatic ship detection is a crucial task within the domain of maritime transportation management. It also has strong real-time image detection capabilities and low hardware In recent years, practical industrial production application have put forward extremely high requirements for its detection accuracy and detection efficiency in the aspect of synthetic aperture radar (SAR) image ship detection. Although these studies have achieved good results, there are generally problems such as low recognition accuracy and human intervention. The ship sample dataset used in this paper is a small sample dataset with limited data, so in order to extract more image feature information from the small sample dataset, the network is first improved to enhance the ability The results show that the average accuracy (AP50) of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1. Airbus provided a large dataset of satellite ship images from SPOT satellite. The experimental methodology is outlined below: initially, we employed a model, denoted as M1 Different from ship detection from synthetic aperture radar (SDSAR) and ship detection from spaceborne optical images (SDSOI), ship detection from visual image (SDVI) has better detection accuracy and real-time performance, which can be widely used in port management, cross-border ship detection, autonomous ship, safe navigation, and other real To overcome these challenges, we propose a You Only Look Once (YOLO)-based optimized ship detection model called YOLO-OSD. 3. Ship detection in top-view drone imagery has various applications, including maritime surveillance, environmental monitoring, and search and rescue operations. 1 Getting Darknet In this paper, we propose a new infrared ship target detection algorithm, YOLO-IRS (YOLO for infrared ship target), based on YOLOv10, which improves detection accuracy while maintaining detection speed. conf (float, optional): Confidence threshold for ship detection. 3). The YOLO model has significantly influenced the field of computer vision, prompting researchers Based on the above analysis, this paper proposes a lightweight detection method LS-YOLO, which can effectively solve multi-scale ship target detection and complex model on satellite SAR images. To solve the problem, we propose a multi-scale SAR ship detection algorithm, named YOLO-MSSD. 7%) achievements. Moreover, the complexity of ship backgrounds An improved ship target detection method YOLO-Ship derived from Y OLOv5 is advanced, which uses MixConv to improve the traditional convolution operation and coordinated attention mechanism, and uses Focal Loss and CIoU Loss to optimize loss functions of the original method. Since then, In order to better adapt to the varying sizes and irregular shapes of ships in ship detection tasks, we designed a set of Anchor However, ship detection using SAR images is still challenging because these images are still affected by different degrees of noise while inshore ships are affected by shore image contrasts. The complexity of changeable marine backgrounds makes ship detection from satellite remote sensing images a challenging task. Download Citation | On Jan 14, 2022, SuYu Zhou and others published YOLO-Ship: A Visible Light Ship Detection Method | Find, read and cite all the research you need on ResearchGate Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. carried out ship target detection from the nonfixed platform [7, 8]. In order to solve this problem, this paper proposes a ship detection method based on an improved YOLO algorithm. Default: 640. The YOLO network has been extensively employed in the field of ship detection in optical images. data; cfg/yolov3-ship. For the ship detection task, we’ll use the same framework. 2%, respectively. First of all, this paper uses structured reparameterization technology to optimize the backbone network. However, wide-area remote sensing satellites sacrifice spatial resolution and spectral resolution to cover a larger sea area, which leads to smaller ship scales, fewer source pixels, and a lack of texture details in the Enhancing maritime alert capabilities relies on effective Synthetic Aperture Radar (SAR) ship detection, often achieved through deep learning techniques. Target detection methods are generally divided into single-stage and two-stage detection methods. Although there are some existing algorithms, it is difficult to balance the accuracy and real-time performance. To address these challenges, this paper introduces an enhanced SAR ship detection model, termed ADV-YOLO, which builds upon the YOLOv8 framework. The accurate detection of ships contributes to the maintenance of maritime rights and interests and the realisation of naval strategy. and YOLO , are based on pre-defined anchors or a grid of feature maps. However, persistent challenges are encountered in SAR ship detection due to factors such as small ship sizes, high noise levels, multiple targets, and scale variations. The technology for detecting maritime targets is crucial for realizing ship intelligence. We propose an enhanced ship detector based on YOLOv8, which reconstructs the neck part of YOLOv8 using a feature fusion network based on AFPN. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. However, due to the high ship density, fore-ground-background imbalance, and varying target sizes, achieving lightweight and high-precision multiscale ship object detection remains a significant challenge. In this paper, we explore the status quo and identify the following Description: Uploads an image or video file for ship detection. YOLO-RSA is a ship detection model that integrates a multi-scale feature pyramid, Small Ship Attention Mechanism, and rotated detection head. 7%), recall (98%), and mAP@0. 50 score (89. pt. Experimental results show that the YOLO-v10-based developed ship fire detection model outperforms several YOLO and other detection models in precision (97. In order to efficiently match ship candidates With the rapid development of marine trade, the demand of ship target detection is increasing. Now, since we have got only one object category (ship), the file will be like this. On the other hand, Zhang Alun Keywords: YOLO network; attention mechanism; loss function; small ship detection; remote sensing 1. This paper presents an advanced extension of the YOLOv8 model to address these challenges. Back in 2019 Airbus published a challenge for ship detectio on Kaggle. For the task of target detection, most of the feature extraction networks are constructed based on VGG-16 (Simonyan and Zisserman, 2014); in that VGG-16 usually performs superiorly on feature extraction and classification. This pre-selected network first identifies and extracts a region of interest from input images. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). Their method employs a meshed input image and depth-separable Ship detection from infrared images occupies an important role in maritime search and tracking applications. To solve the problems of low ship detection accuracy and easy missing detection of small targets in complex marine environment, an improved YOLOv3 target detection algorithm is proposed. However, ship detection by camera-equipped UAVs faces challenges when it comes to multi-viewpoints, multi-scales, environmental variability, and dataset scarcity. An improved YOLOv8 is presented to increase the representational capacity of the detection network for SAR ship targets (Section. The model introduces the following optimizations: First, to address the difficulty of detecting weak and small targets, the Swin Transformer is Despite its importance, SAR ship detection confronts several challenges, including the small size of ship targets, unclear contours, complex background noise, and variable scales of ships. : LMO-YOLO: A SHIP DETECTION MODEL FOR LOW-RESOLUTION OPTICAL SATELLITE IMAGERY 4119 retention of information. While, the existing target detection methods have some problems, such as low precision, poor real-time, lack of detection and false detection. YOLT was presented as a modification of YOLOv2 in order to detect small objects, Aiming at the problem of insufficient feature extraction, low precision, and recall in sea surface ship detection, a YOLOv5 algorithm based on lightweight convolution and attention mechanism is proposed. Additionally, we reported object detection results on the COCO test-dev dataset in Table 5. Specifically, we enhance the C3 module of YOLOv5 by incorporating Coordinate Attention (CA) and a bottleneck mechanism, forming the CB-C3 module. However, detecting multi-scale ship targets in complex backgrounds leads to issues of false positives and missed detections, posing challenges for This is the official release of LEVIR-Ship, which is a dataset for tiny ship detection under medium-resolution remote sensing images - WindVChen/LEVIR-Ship. This paper presents YOLO-Vessel, a ship detection model built upon YOLOv7, which incorporates several innovations to improve its performance. This model uses MixConv to improve the traditional convolution operation and coordinated attention mechanism, and uses Focal Loss and CIoU This results in suboptimal performance in ship detection, including potential misses and false detections. The SAR images is derived from SAR-Ship-Dataset. 3 illustrates the architecture of YOLO. YOLO employs a direct prediction approach for relative positioning. proposed an efficient ship detection model based on YOLOX named YOLO-Ship Detection (YOLO-SD). Fig. However, there are still issues, such as missed detection and incorrect identification when performing Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this This research focuses on ship detection using variety of YOLO with 3 different datasets, such as model 1 using Sentinel-1 image with RGB composite which Sigma Nought VV polarization for red, Sigma Nought VH polarization for green, and Sigma Nought VV/VH polarization for blue. 8%. In this notebook, we will cover the following: In this article, an infrared ship rotating target detection algorithm FMR-YOLO based on synthetic fog and multiscale weighted fusion is proposed. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models Darknet requires certain files to know how and what to train. CNN-Based Ship Detection Methods In recent years, encouraged by the great success of deep learningmethods,manyCNN . However, existing SAR ship detection models face challenges due to their large sizes, rendering them impractical for deployment on resource-constrained devices. This name is shown over the bounding box in the output. However, the development of ship detection techniques is vastly behind other detection techniques, such as face detection, pedestrian detection, traffic sign/light detection, text detection, etc. However, existing ship target detection models often encounter missed detections and struggle to achieve both high accuracy and real-time performance at the same time. Compared to two-stage detection models, this approach simplifies the entire detection process by The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships convolutional YOLO (Lk-YOLO) detec tion model based on Anchor fr ee for one-stage ship detection. Specifically, the algorithm consisted of two stacked GhostNet to refine and capture the image features, so as to Unmanned aerial vehicles (UAVs) with cameras offer extensive monitoring capabilities and exceptional maneuverability, making them ideal for real-time ship detection and effective ship management. For YOLOv5, the backbone is designed using the New CSP-Darknet53 structure, a modification of the Darknet architecture used in A lightweight ship object detection model called YOLOv7-Ship to perform end-to-end ship detection in complex marine environments and has a lightweight feature with a detection speed of 75 frames per second, which can meet the need for real-time detection in complex marine environments to a certain extent. our AM YOLO algorithm achieves a target detection accuracy of 43. Ship detection technology plays a major role in maritime traffic safety and waterway management. The proposed model incorporates space-to-depth In recent years, deep learning has made breakthroughs in the field of computer vision, the single-stage detection algorithm represented by You Only Look Once (YOLO) has achieved satisfying detection results in SAR ship target detection. To overcome these challenges, we introduce RD-YOLO, a Zang et al. 7%, 0. The later development of YOLO, particularly with YOLOv3 and YOLOv5, has established new benchmarks for object detection, and these models have become widely used for ship detection in both optical and synthetic aperture radar (SAR) imagery [16,17,18]. research on ship identification. 1 AP) and These modules synergistically enhance the performance of the AM YOLO model in ship instance segmentation. Therefore, we choose YOLOv7 as the baseline and propose an end-to-end feature fusion and feature enhancement YOLO (FE-YOLO). imgsz (integer, optional): The image size for processing. ; Path_model (string, optional): The path to the YOLO model weights file. 1%, and 0. thhjywyj hpxeui yqgxn pghvly oiiq oao nylc lrbeqk jjqkx lxyard