Research on an Improved YOLOv8 Algorithm for Water Surface Object Detection

To improve the accuracy of water surface debris detection under complex backgrounds and strong reflection conditions, this paper proposes a lightweight improved object detection algorithm based on YOLOv8n. Since shallow features are most sensitive to low-level visual interference such as water surface reflections, this paper adopts the C2f_RFAConv module to enhance the model’s robustness to reflection interference regions. By adopting the Four-Detect-Adaptively Spatial Feature Fusion (ASFF) module, the model’s perception capabilities for objects of different scales (especially small objects) are improved. To avoid excessive computational complexity caused by the addition of new components, this paper adopts the lightweight Slim-neck structure. The Minimum Point Distance Intersection over Union (MPDIoU) loss function effectively improves the localization accuracy of detected objects by directly minimizing the Euclidean distance between the predicted bounding box and the ground truth bounding box. Experiments conducted on the publicly available water surface debris dataset provided by the Roboflow Universe platform show that the proposed method achieves 94.5% and 58.6% on the mAP@0.5 and mAP@0.5:0.95 metrics, respectively, representing improvements of 2.27% and 5.21% over the original YOLOv8 model.