YOLO-CDAW: an improved underwater garbage detection model based on YOLOv8n

Due to the complex marine environment, low visibility, object occlusion, and limited underwater resources and environment, the task of detecting marine garbage is extremely difficult. In this regard, this paper suggests an underwater garbage detection model YOLO-CDAW utilizing the improved YOLOv8n model. First, to improve the separation between underwater garbage and occluders, the CA module is inserted to the YOLOv8n model’s backbone network. Secondly, integrates the CoordConv module alongside the C2f module of the YOLOv8n model, proposes a new CD_C2f module, then utilize it to switch out some of the backbone natework’s C2f modules. This module enables the model to get full utilization of the position data relating to the object without increasing the model calculation amount and parameter amount, thereby improving the network's feature extraction ability. Finally, to strengthen the model’s capacity for generalization, the CIoU loss function is swapped out with the WIoU loss function. Experimental results show that the upgrated YOLO-CDAW model’s mAP50/% and mAP50:95/% are 2% and 1.8% greater compared to the original model for each, with an inference speed of 3.3ms and only 3.03M parameters, which fulfills the need for real-time underwater garbage detection within the limits of resource and environmental constraints.