信阳师范大学学报（自然科学版）2026，Vol.39Issue(1)：52-57,6.DOI:10.3969/j.issn.2097-583X.2026.01.007
基于改进YOLOv8的水下垃圾检测研究
Research on underwater garbage detection based on improved YOLOv8
摘要
Abstract
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.关键词
水下垃圾检测/多目标检测/特征融合/多尺度特征检测Key words
underwater waste detection/multi object detection/feature fusion/multi scale feature detection分类
信息技术与安全科学引用本文复制引用
李艳灵,赵添宇,李佳漫,梁庆琪,杨志鹏,陈重阳..基于改进YOLOv8的水下垃圾检测研究[J].信阳师范大学学报（自然科学版）,2026,39(1):52-57,6.基金项目
国家自然科学基金项目(62506324) （62506324）
河南省科技计划项目(252102211025) （252102211025）
河南省研究生精品教材项目(YJS2025JC30) （YJS2025JC30）
河南省教师教育课程改革研究项目(2026-JSJYZD-019) （2026-JSJYZD-019）
