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1 June 2023 Research on underwater seafood target detection algorithm based on improved YOLOv5
Yingjie Han, Weisheng Liu
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Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127181G (2023) https://doi.org/10.1117/12.2681724
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China
Abstract
The underwater environment of the sea bottom is complex, and it is easy to be affected by light, underwater impurities and shooting angle. Because of the dense and mutual occlusion of underwater targets, the collected images are not clear, fuzzy and easy to be resolved, which affects the accuracy of target detection and recognition. Aiming at the above problems, this paper proposes an improved YOLOV5 method for underwater seafood target detection. Firstly, the C3 module of high-level features is fused with swin transformer to create the C3STR module, and the improved C3STR module can enlarge the receptive field of the network, obtain the global information better and increase the ability of feature extraction, the DC3 module is constructed by adding convolution to the low-level feature C3 module, which improves the model detection ability with a little more computation. Finally, the network feature fusion module is improved, which integrates the deep-level information with the shallow-level information, the combination of semantic information and position information can improve the precision of model recognition and enhance the ability of target detection. The experimental results show that the detection accuracy of the improved YOLOv5 algorithm is increased to 88.3%, which is 2% higher than that of the original YOLOv5 algorithm.
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Yingjie Han and Weisheng Liu "Research on underwater seafood target detection algorithm based on improved YOLOv5", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127181G (1 June 2023); https://doi.org/10.1117/12.2681724
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KEYWORDS
Target detection
Submerged target detection
Feature extraction
Feature fusion
Convolution
Transformers
Target recognition
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