WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention

Abstract

Accurate marine benthos detection is a technical prerequisite for underwater robots to achieve automated fishing. Considering the challenges of poor underwater imaging conditions during the actual fishing process, where small objects are easily occluded or missed, we propose WDS-YOLO, an advanced model designed for marine benthos detection, built upon the YOLOv8n architecture. Firstly, the convolutional module incorporated with wavelet transform was used to enhance the backbone network, thereby expanding the receptive field of the model and enhancing its feature extraction ability for marine benthos objects under low visibility conditions. Secondly, we designed the DASPPF module by integrating deformable attention, which dynamically adjusts the attention domain to enhance feature relevance to targets, reducing irrelevant information interference and better adapting to marine benthos shape variations. Finally, the SF-PAFPN feature fusion structure was designed to enhance the model’s ability to detect smaller object features while mitigating false positives and missed detections. The experimental results demonstrated that the proposed method achieved 85.6% mAP@50 on the URPC dataset, representing a 2.1 percentage point improvement over the YOLOv8n model. Furthermore, it outperformed several mainstream underwater object detection algorithms, achieving a detection speed of 104.5 fps. These results offer significant technical guidance for advancing intelligent fishing systems powered by underwater robotic technologies.