Improved YOLOv8-based Marine Life Detection Algorithm

Abstract

Aiming at the problems of insufficient feature extraction ability for small targets, complex image background, and low detection accuracy in marine life detection, this paper proposes a marine life detection algorithm SGW-YOLOv8 based on the improvement of YOLOv8. First, the Adaptive Fine-Grained Channel Attention (FCA) module is fused with the backbone layer of the YOLOv8 network to improve the feature extraction ability of the model. This paper uses the YOLOv8 network backbone layer to improve the feature extraction capability of the model. Second, the Efficient Multi-Scale Attention (C2f_EMA) module is replaced with the C2f module in the Neck layer of the network to improve the detection performance of the model for small underwater targets. Finally, the loss function is optimized to Weighted Intersection over Union (WIoU) to replace the original loss function, so that the model is better adapted to the target detection task in the complex ocean background. The improved algorithm has been experimented with on the Underwater Robot Picking Contest (URPC) dataset, and the results show that the improved algorithm achieves a detection accuracy of 84.5, which is 2.3% higher than that before the improvement, and at the same time, it can accurately detect the small-target marine organisms and adapts to the task of detecting marine organisms in various complex environments.