西安工程大学学报2025，Vol.39Issue(2)：39-46,8.DOI:10.13338/j.issn.1674-649x.2025.02.005
轻量化YOLOv5s的水下垃圾检测方法
Underwater garbage detection method using lightweight YOLOv5s
摘要
Abstract
Aimed at the problems of low recognition accuracy and low speed of small target detec-tion algorithm,an improved lightweight YOLOv5s marine underwater garbage detection method,namely YOLOV5s-MCS,was proposed.Although the backbone network of YOLOv5s itself had a strong feature extraction capability,in the face of small underwater garbage fragments,the contin-uous deepening of convolution might lead to the loss of some features and similar redundancy be-tween feature graphs.Therefore,in order to minimize the number of parameters and improve the speed of the model,MobileNetv3-Small was proposed to optimize the backbone network in YOLOv5s.Secondly,coordinate attention(CA)was used to replace the original attention mecha-nism SE in MobileNetv3-Small.In this way,not only the information between channels,but also the position information of horizontal and vertical coordinates could be obtained.Finally,the CIoU loss function was optimized to SIoU loss function.By improving the overall network model,the detection accuracy reaches 85.7%while ensuring the model's lightweight,and the number of floating-point operations and parameters are reduced to 1/5 and 1/7 of the benchmark YOLOv5s network,respectively.关键词
海洋水下垃圾检测/YOLOv5s/轻量化/注意力机制/损失函数Key words
marine underwater garbage detection/YOLOv5s/lightweight/attention mechanism/loss function分类
计算机与自动化引用本文复制引用
王延年,李英,廉继红..轻量化YOLOv5s的水下垃圾检测方法[J].西安工程大学学报,2025,39(2):39-46,8.基金项目
陕西省科技厅一般项目(2022GY-053) （2022GY-053）
