Sea Cucumber Detection Algorithm Based on Deep Learning

Zhang, Lan; Xing, Bowen; Wang, Wugui; Xu, Jinliang

doi:10.3390/s22155717

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The P-R curve can be used to reflect the performance of the model. The improved YOLOv7-GDAW model has a larger area enclosed by the two coordinate axes of the P-R curve 40 , and its Break-Even Point is closer to the coordinate point (1,1). Based on these comparisons, it can be shown that the improved ship detection model YOLOv7-GDAW system performs better.…”

Section: Validation Of the Improved Yolov7 Modelmentioning

confidence: 89%

Design of Real-time Ship Detection Strategy Based on Modified YOLOv7 Architecture

Xing,

Wang,

xing

et al. 2024

Preprint

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Ship detection is crucial in inland waterway shipping management, and it is not easy to balance accuracy and real-time performance in complex water conditions. This paper proposes a real-time ship detection method based on improved YOLOv7 to address the problem. Firstly, GhostNet is introduced into the backbone network for feature extraction, and then distribution shifting convolution is introduced into the feature fusion network to achieve a lightweight model. Secondly, an attention mechanism is introduced into the feature fusion network to compensate for the accuracy loss caused by the lightweight model. Finally, the loss function is improved to make the detection model more applicable to the ship dataset. Compared with the traditional YOLOv7 detection model, the experimental results of the HPRship dataset show that the computation volume is reduced by 3.88 × 10 10 , the model parameter volume is reduced by 5.7 × 10 6 , and the detection accuracy mAP0.5 is increased by 0.7% to 98.80%. YOLOv7-GDAW model achieves a good balance between lightweight and detection accuracy, allowing it to accurately and timely complete ship detection tasks. It is suitable for deployment on small devices with limited storage and computing capacity.

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Section: Validation Of the Improved Yolov7 Modelmentioning

confidence: 89%

Design of Real-time Ship Detection Strategy Based on Modified YOLOv7 Architecture

Xing,

Wang,

xing

et al. 2024

Preprint

Self Cite

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“…Li et al [33] added an extra detection head to the YOLOv5 model to improve multi-scale detection and small target detection accuracy. Zhang et al [34] combined an attention mechanism to learn target features and increase the receptive field to improve the detection accuracy and robustness of underwater small target detection. Yao et al [35,36] used residual networks as a backbone network to improve the efficiency of sea cucumber feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Underwater Target Detection Based on Parallel High-Resolution Networks

Bao,

Guo,

Wang

et al. 2023

Sensors

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A parallel high-resolution underwater target detection network is proposed to address the problems of complex underwater scenes and limited target feature extraction capability. First, a high-resolution network (HRNet), a lighter high-resolution human posture estimation network, is used to improve the target feature representation and effectively reduce the semantic information lost in the image during sampling. Then, the attention module (A-CBAM) is improved to capture complex feature distributions by modeling the two-dimensional space in the activation function stage through the introduction of the flexible rectified linear units (FReLU) activation function to achieve pixel-level spatial information modeling capability. Feature enhancement in the spatial and channel dimensions is performed to improve understanding of fuzzy targets and small target objects and to better capture irregular and detailed object layouts. Finally, a receptive field augmentation module (RFAM) is constructed to obtain sufficient semantic information and rich detail information to further enhance the robustness and discrimination of features and improve the detection capability of the model for multi-scale underwater targets. Experimental results show that the method achieves 81.17%, 77.02%, and 82.9% mean average precision (mAP) on three publicly available datasets, specifically underwater robot professional contest (URPC2020, URPC2018) and pattern analysis, statistical modeling, and computational learning visual object classes (PASCAL VOC2007), respectively, demonstrating the effectiveness of the proposed network.

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“…Giving an additional detection head to the YOLOv5 model could also enhance the multiple scale detection and improve the detection accuracy of small targets [ 17 ]. Attention mechanisms were effective in learning object features and increasing the receptive field could improve the detection accuracy and robustness in recognizing underwater small targets [ 18 ]. Furthermore, many works were reported on the modification of feature extraction networks which improved the ability of the feature representation of underwater targets.…”

Section: Introductionmentioning

confidence: 99%

In Situ Sea Cucumber Detection across Multiple Underwater Scenes Based on Convolutional Neural Networks and Image Enhancements

Wang

et al. 2023

Sensors

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Recently, rapidly developing artificial intelligence and computer vision techniques have provided technical solutions to promote production efficiency and reduce labor costs in aquaculture and marine resource surveys. Traditional manual surveys are being replaced by advanced intelligent technologies. However, underwater object detection and recognition are suffering from the image distortion and degradation issues. In this work, automatic monitoring of sea cucumber in natural conditions is implemented based on a state-of-the-art object detector, YOLOv7. To depress the image distortion and degradation issues, image enhancement methods are adopted to improve the accuracy and stability of sea cucumber detection across multiple underwater scenes. Five well-known image enhancement methods are employed to improve the detection performance of sea cucumber by YOLOv7 and YOLOv5. The effectiveness of these image enhancement methods is evaluated by experiments. Non-local image dehazing (NLD) was the most effective in sea cucumber detection from multiple underwater scenes for both YOLOv7 and YOLOv5. The best average precision (AP) of sea cucumber detection was 0.940, achieved by YOLOv7 with NLD. With NLD enhancement, the APs of YOLOv7 and YOLOv5 were increased by 1.1% and 1.6%, respectively. The best AP was 2.8% higher than YOLOv5 without image enhancement. Moreover, the real-time ability of YOLOv7 was examined and its average prediction time was 4.3 ms. Experimental results demonstrated that the proposed method can be applied to marine organism surveying by underwater mobile platforms or automatic analysis of underwater videos.

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Sea Cucumber Detection Algorithm Based on Deep Learning

Cited by 5 publications

References 32 publications

Design of Real-time Ship Detection Strategy Based on Modified YOLOv7 Architecture

Design of Real-time Ship Detection Strategy Based on Modified YOLOv7 Architecture

Underwater Target Detection Based on Parallel High-Resolution Networks

In Situ Sea Cucumber Detection across Multiple Underwater Scenes Based on Convolutional Neural Networks and Image Enhancements

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