YOLOTrashCan: A Deep Learning Marine Debris Detection Network

Monitoring marine debris has long been a challenging issue owing to the complex and changeable underwater environment. To fast and accurately detect marine debris, in this article, a novel object detection network termed as YOLOTrashCan is proposed for detecting underwater marine debris. The YOLOTrashCan model consists of feature enhancement and feature fusion. In the feature enhancement part, the ECA_DO-Conv_CSPDarknet53 backbone, which combines efficient channel attention (ECA) module and depthwise over-parameterized convolutional (DO-Conv), is proposed to extract the depth semantic features of marine debris. In the feature fusion part, the DPMs_PixelShuffle_PANET module is presented to improve the detection ability for marine debris, where dilated parallel modules (DPMs) with multiscale dilated rate are designed as enhanced feature modules for different scale objects of marine debris. Notably, the size of the network is only 214 MB using the DPMs' method. Extensive experiments and thorough analysis are validated on the TrashCan 1.0 dataset. Experimental results show that the proposed algorithm not only improves the detection accuracy of underwater marine debris but also reduces the size of the network model.