Enhanced Floating Debris Detection Algorithm Based on CDW-YOLOv8

Abstract The issue of floating debris on water surfaces is becoming increasingly prominent, posing significant threats to aquatic ecosystems and human habitats. The detection of floating debris is impeded by complex backgrounds and water currents, resulting in suboptimal detection accuracy. To enhance detection effectiveness, this study presents a floating debris detection algorithm rooted in CDW-YOLOv8. Firstly, the study augments the original C2f module by incorporating the Coordinate Attention (CA) mechanism, resulting in the C2f-CA module, to boost the model's sensitivity to target locations. Secondly, the study substitutes the standard Upsample module with the DySample module to diminish model parameters and increase flexibility. Furthermore, the study incorporates a small object detection layer to enhance the detection performance of small floating debris. Lastly, the Complete-IOU (CIOU) loss function is substituted by the Focaler-Wise-IOU v3 (Focaler-WIoUv3) loss function, which aims to minimize the impact of low-quality anchor boxes and improve regression accuracy. Experimental results demonstrate that the improved CDW-YOLOv8 algorithm has realized a comprehensive performance improvement in accuracy, recall rate, mAP@0.5, and mAP@0.5:0.95, noting increases of 2.9%, 0.6%, 2.5%, and 1.5%, respectively, relative to the original YOLOv8 algorithm. This offers a robust reference for the intelligent detection and identification of floating debris on water surfaces.