Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images

Chen, Liqiong; Shi, Wenxuan; Deng, Dexiang

doi:10.3390/rs13040660

Cited by 74 publications

(39 citation statements)

References 63 publications

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“…Tian et al [83] proposed a detection framework based on remote sensing image combining image enhancement module and dense feature reuse module to improve the object detection capability. Chen et al [84] proposed an improved YOLOv3 based on an attention mechanism for fast and accurate ship detection, which accelerates detection speed to achieve realtime detection effect and improves the level of maritime surveillance.…”

Section: High-resolution Satellite Image Surveillancementioning

confidence: 99%

“…Many background subtraction and object detection methods are very difficult in the video stream. For example, [84] designs a multiclass ship dataset (MSD) to highlight the difference between the ship and the background; it can improve the accuracy of tiny ship detection.…”

Section: Background Subtractionmentioning

confidence: 99%

“…It is necessary to propose a maritime environment image/video perception based on an improved regressive deep convolution network. YOLO series architecture is always the first neural network to be considered, for example, [12,71,74,75,77,79,82,84,88,123,126,[130][131][132]; these improvements contributed to a stronger baseline cross YOLO series detector.…”

Section: Model Comparisonmentioning

confidence: 99%

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Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

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We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

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Section: High-resolution Satellite Image Surveillancementioning

confidence: 99%

Section: Background Subtractionmentioning

confidence: 99%

Section: Model Comparisonmentioning

confidence: 99%

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Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

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“…However, the single feature extraction network and FPN structure did not fully consider the smallscale ship's detection. Chen et al used the attention mechanism to propose an improved YOLO-V3 (ImYOLO-V3) [22], and embedding the attention module into YOLO-V3 effectively improved the accuracy of detection, but there is no further optimization of the speed of the model. Jie et al introduced the K-means clustering algorithm and soft nonmaximum suppression algorithm to optimize YOLO-V3 to make it more suitable for the ship scene [23], but the improved method proposed by it belongs to the engineering tuning technology, and there is no solution to the accuracy problem of ship detection from the perspective of model construction.…”

Section: Introductionmentioning

confidence: 99%

ShipYOLO: An Enhanced Model for Ship Detection

Han

Zhao

Ning

et al. 2021

Journal of Advanced Transportation

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The application of ship detection for assistant intelligent ship navigation has stringent requirements for the model’s detection speed and accuracy. In response to this problem, this study uses an improved YOLO-V4 detection model (ShipYOLO) to detect ships. Compared to YOLO-V4, the model has three main improvements. Firstly, the backbone network (CSPDarknet) of YOLO-V4 is optimized. In the training process, the 3 × 3 convolution, 1 × 1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. In the inference process, the branch parameters are combined to form a new backbone network named RCSPDarknet, which improves the inference speed of the model while improving the accuracy. Secondly, in order to solve the problem of missed detection of the small-scale ships, we designed a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. Finally, we use the attention mechanism and Resnet’s shortcut idea to improve the feature pyramid structure (PAFPN) of YOLO-V4 and get a new feature pyramid structure named AtFPN. The structure effectively improves the model’s feature extraction effect for ships of different scales and reduces the number of model parameters, further improving the model’s inference speed and detection accuracy. In addition, we have created a ship dataset with a total of 2238 images, which is a single-category dataset. The experimental results show that ShipYOLO has the advantage of faster speed and higher accuracy even in different input sizes. Considering the input size of 320 × 320 on the PC equipped with NVIDIA 1080Ti GPU, the FPS and mAP@5 : 5:95 (mAP90) of ShipYOLO are increased by 23.7% and 13.6% (10.6%), respectively, with an input size of 320 × 320, ShipYOLO, compared to YOLO-V4.

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“…With the rapid development of artificial intelligence, object detection has witnessed great progress in many application scenarios [1]. One of the applications is the ship detection from remote sensing images [2][3][4][5][6][7]. In the past two decades, a number of commercial and government optical satellites have been deployed to observe the earth, which provide massive amounts of optical remote sensing images [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Elongated Small Object Detection from Remote Sensing Images Using Hierarchical Scale-Sensitive Networks

Huang

Zeng

et al. 2021

Remote Sensing

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The detection of elongated objects, such as ships, from satellite images has very important application prospects in marine transportation, shipping management, and many other scenarios. At present, the research of general object detection using neural networks has made significant progress. However, in the context of ship detection from remote sensing images, due to the elongated shape of ship structure and the wide variety of ship size, the detection accuracy is often unsatisfactory. In particular, the detection accuracy of small-scale ships is much lower than that of the large-scale ones. To this end, in this paper, we propose a hierarchical scale sensitive CenterNet (HSSCenterNet) for ship detection from remote sensing images. HSSCenterNet adopts a multi-task learning strategy. First, it presents a dual-direction vector to represent the posture or direction of the tilted bounding box, and employs a two-layer network to predict the dual direction vector, which improves the detection block of CenterNet, and cultivates the ability of detecting targets with tilted posture. Second, it divides the full-scale detection task into three parallel sub-tasks for large-scale, medium-scale, and small-scale ship detection, respectively, and obtains the final results with non-maximum suppression. Experimental results show that, HSSCenterNet achieves a significant improved performance in detecting small-scale ship targets while maintaining a high performance at medium and large scales.

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Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images

Cited by 74 publications

References 63 publications

Survey on Deep Learning-Based Marine Object Detection

Survey on Deep Learning-Based Marine Object Detection

ShipYOLO: An Enhanced Model for Ship Detection

Elongated Small Object Detection from Remote Sensing Images Using Hierarchical Scale-Sensitive Networks

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