关键词: 目标检测, 水下垃圾, 多尺度特征融合, YOLOv5, GSConv, SimAM

Abstract: To address the limitations of underwater image acquisition such as insufficient light， high noise and unclear object recognition， which lead to the ineffectiveness of existing object detection algorithms， an underwater garbage object detection algorithm based on improved YOLOv5 is proposed. The purpose of the improved object detection algorithm is to achieve more accurate detection and removal of underwater plastic trash from the ocean. The improved algorithm containes some improvements：using the Contrast Limited Adaptive Histogram Equalization（CLAHE） algorithm to enhance data features， which reduces the difficulty of feature extraction and enables the network to be detected more flexibly and more accurately； introducing a parameter-free attention module SimAM， using the lightweight convolution method GSConv to enhance network extraction capability while reducing model computation; At the same time， multi-scale feature fusion detection is added to solve the problem of small target location of underwater debris. Numbers of experiments are conducted based on MarineTrash which is a self-built real underwater environmental litter dataset， the results show that the improved method has good performance， in which the accuracy is increased by 4.3 percentage points， the mAP is increased by 3.5 percentage points， the GFLOPs is reduced by 0.3， and the model weight is only 13.9 MB， which is 0.6 MB lower than the baseline. The research on the underwater trash detection algorithm based on the improved YOLOv5 provides sufficient technology for deploying and installing detectors in Autonomous Underwater Vehicles （AUVs） to achieve detection and automatic removal of marine underwater trash and maintain the marine ecosystem.

Key words:  , object detection, underwater trash, multi-scale feature fusion, YOLOv5, GSConv, SimAM