Efficient Underwater Garbage Detection Using GSConv Enhanced YOLOv8 with GD Mechanism - Fu, Xiaolong; Gao, Xiaolong; Fu, Zufeng; Yang, Zhuang (Informatica, 2025)
Due to low visibility, scale variation, and complex backgrounds, detecting marine debris in underwater environments remains highly challenging. To address these issues, we propose YOLO-GGS, a lightweight yet high-performance object detector built upon YOLOv8. The framework incorporates three key innovations. First, the Gather-and-Distribute (GD) mechanism from Gold-YOLO is introduced into the neck, which unifies multi-scale feature aggregation while selectively injecting global context, thereby enhancing object perception across different scales. Second, GSConv-based hybrid convolutions are deployed in both the backbone and the injection module, effectively balancing rich channel interactions with reduced computational complexity. Third, a Slim-Neck design simplifies the feature fusion path by eliminating redundant operations, thus improving inference speed. Comprehensive experiments on the J-EDI and Brackish underwaterdatasets demonstrate the superior performance of YOLO-GGS, achieving mAP@0.5:0.95 values of 88.5% and 84.7%, which represent improvements of 4% and 2.5% over the baseline model, respectively. Moreover, real-time evaluation shows that YOLO-GGS reaches an inference speed of 108.4 FPS. These resultshighlight YOLO-GGS as an efficient and accurate solution for underwater debris detection, offering substantial potential for deployment on Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs).