Research on underwater garbage detection based on improved YOLOv8

To address the core challenge that the existing object detection algorithms were constrained by issues such as low light， low image resolution， and dense small objects in underwater environments， an improved YOLOv8 algorithm for underwater garbage object detection was proposed. The algorithm introduced self‑calibrated convolution into the feature fusion module of YOLOv8， used the method of grouping convolution for multi scale feature extraction， and expanded the receptive field of the network through down-sampling operation， to improve the multi scale feature fusion and detection ability of the model， and more accurately identify underwater garbage. The experiments were carried out on the Seaclear Marine Debris data set and TrashCan data set. Compared with YOLOv8， the detection accuracy of the improved model on the Seaclear Marine Debris data set was improved by 1.5 percent point， mAP values increased by 1.1 percent point. On the TrashCan dataset， the detection accuracy was increased by 2.4 percent point， and the mAP value was increased by 0.7 percent point. Experimental results showed that the proposed method could maintain high detection accuracy in complex underwater environment， and could meet the actual needs of underwater garbage detection.