Floating debris detection algorithm based on modified YOLOv8 network

The detection technology for floating debris plays a vital role in environmental protection tasks. This paper addresses challenges including surface reflection interference, shore background interference, and insufficient accuracy in recognizing small objects. To figure out these issues, an enhanced GST-YOLOv8 object detection method is proposed to improve detection accuracy in this paper. The improvement is achieved by introducing a global attention layer in the backbone network to facilitate the concentration of critical image information, enhancing processing efficiency. Additionally, a small object perception layer is incorporated into the neck network to enhance the perception of shallow-level information during detection. This perception layer leads to improved accuracy in detecting small objects. Comparative experiments and ablation experiments conducted on a dedicated public dataset illustrate the efficacy of the enhanced method.