Efficient detection method of deep-sea netting breakage based on attention and focusing on receptive-field spatial feature

Yu, Guoyan; Su, Jinping; Luo, Yingtong; Chen, Zejia; Chen, Qibo; Chen, Shuaixing

doi:10.1007/s11760-023-02806-6

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Shen et al [17] proposed the crisscross global interaction strategy (CGIS) for versions of YOLO detectors in order to minimize the interference of the underwater background with the detected target. Yu et al [18] proposed YOLOv7-net, which added bi-level routing attention (BRA) and a new coordinated attention module (RFCAConv) to YOLOv7. This improved the detection of broken nets in complex marine environments.…”

Section: Introductionmentioning

confidence: 99%

Underwater Object Detection in Marine Ranching Based on Improved YOLOv8

Jia,

Lv,

Chen

et al. 2023

JMSE

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The aquaculture of marine ranching is of great significance for scientific aquaculture and the practice of statistically grasping existing information on the types of living marine resources and their density. However, underwater environments are complex, and there are many small and overlapping targets for marine organisms, which seriously affects the performance of detectors. To overcome these issues, we attempted to improve the YOLOv8 detector. The InceptionNeXt block was used in the backbone to enhance the feature extraction capabilities of the network. Subsequently, a separate and enhanced attention module (SEAM) was added to the neck to enhance the detection of overlapping targets. Moreover, the normalized Wasserstein distance (NWD) loss was proportionally added to the original CIoU loss to improve the detection of small targets. Data augmentation methods were used to improve the dataset during training to enhance the robustness of the network. The experimental results showed that the improved YOLOv8 achieved the mAP of 84.5%, which was an improvement over the original YOLOv8 of approximately 6.2%. Meanwhile, there were no significant increases in the numbers of parameters and computations. This detector can be applied on platforms for seafloor observation experiments in the field of marine ranching to complete the task of real-time detection of marine organisms.

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Section: Introductionmentioning

confidence: 99%

Underwater Object Detection in Marine Ranching Based on Improved YOLOv8

Jia,

Lv,

Chen

et al. 2023

JMSE

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Autonomous net inspection and cleaning in sea-based fish farms: A review

Fu,

Liu,

et al. 2024

Computers and Electronics in Agriculture

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FishFocusNet: An improved method based on YOLOv8 for underwater tropical fish identification

Lu,

Zhu,

Guo

et al. 2024

IET Image Processing

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Accurately identifying tropical fish serves as a crucial indicator, offering an insight into the state of marine biodiversity and the condition of coral reef ecosystems. However, the current detection networks are prone to omission and misidentification due to occlusion between fish and the complex underwater environment. This paper proposes a modified approach named FishFocusNet, in which alterable kernel convolution modules, asymptotic feature pyramid network (AFPN), and Shape‐IoU are integrated into YOLOv8. To extract a more comprehensive set of fish features, AKConv modules with arbitrary kernel sizes are proposed to take the place of the conventional fixed‐shaped kernels in the backbone for downsampling. AFPN is adopted as the feature integration structure in the neck, which enhances feature fusion and adaptive spatial fusion between non‐adjacent layers. In the detector head, Shape‐IoU is employed to achieve precise localization of fish targets. The superiorities of these modifications are proved by ablation experiments and comparative experiments. The experimental results show that the optimized approach obtained an mAP of 81.8% accompanied by 2.4 MB parameters and 3.6 GB FLOPS. Meanwhile, compared with more complicated models of similar scale, the proposed method can enhance recognition accuracy to 84.2% and significantly reduce computational costs.

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Efficient detection method of deep-sea netting breakage based on attention and focusing on receptive-field spatial feature

Cited by 3 publications

References 22 publications

Underwater Object Detection in Marine Ranching Based on Improved YOLOv8

Underwater Object Detection in Marine Ranching Based on Improved YOLOv8

Autonomous net inspection and cleaning in sea-based fish farms: A review

FishFocusNet: An improved method based on YOLOv8 for underwater tropical fish identification

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