Underwater Biological Detection Based on YOLOv4 Combined with Channel Attention

Abstract

Due to the influence of underwater visual characteristics on the observation of underwater creatures, the traditional object detection algorithm is ineffective. In order to improve the robustness of underwater biological detection, based on the YOLOv4 detector, this paper proposes an underwater biological detection algorithm combined with the channel attention mechanism. Firstly, the backbone feature extraction network CSPDarknet53 of YOLOv4 was improved, and a residual block combined with the channel attention mechanism was proposed to extract the weighted multi-scale effective features. Secondly, the weighted features were repeatedly extracted through the feature pyramid to separate the most significant weighted features. Finally, the most salient weighted multi-scale features were used for underwater biological detection. The experimental results show that, compared with YOLOv4, the proposed algorithm improved the average accuracy of the Brackish underwater creature dataset detection by 5.03%, and can reach a detection rate of 15fps for underwater creature video clips. Therefore, it is feasible to apply this method to the accurate and real-time detection of underwater creatures. This research can provide technical reference for the exploration of marine ecosystems and the development of underwater robots.