| Citation: | CHEN Y L，DONG S J，SUN S Z，et al. Improved YOLOv5s low-light underwater biological target detection algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：499-507 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0322
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A real-time detection method of low-light underwater biological target based on improved YOLOv5s, known as YOLOv5s-underwater, was proposed to address the issue of low biometric recognition accuracy caused by the significant attenuation of light in water, the complex image environment, and the movement of shooting equipment in the process of underwater optical image target detection. Firstly, to solve the problem of weak underwater light attenuation, the contrast-limited adaptive histogram equalization (CLAHE) algorithm is introduced to preprocess the input image, which solves the problems of color distortion and image roughness. Secondly, the spatial pyramid pooling fast (SPPF) module is proposed to solve the problems of low discrimination and serious feature loss of underwater objects in the complex low-light underwater image environment. Thirdly, a Swin-Transformer module based on the spin window is proposed to improve the generalization ability of the model. Finally, the network model structure is modified to improve the detection ability of small underwater targets. Simulation and experiment prove that the proposed method improves the detection accuracy by 30.7% compared with YOLOv5s. Results from experiments support the method’s efficacy.
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