人民黄河2024，Vol.46Issue(7)：85-91,7.DOI:10.3969/j.issn.1000-1379.2024.07.015
基于改进YOLOv5s的水面漂浮物检测算法研究
Research on Detection Algorithm of Floating Objects on Water Surface Based on Improved YOLOv5s
摘要
Abstract
The water surface image has complex features such as water wave disturbances,light reflection and shoreline reflections,which cause existing object detection algorithms to fail to recognize floating objects.Therefore,a water surface floating object detection algorithm based on an improved YOLOv5s was proposed.By modifying the neck structure of the network,adding object detection layers to improve the detection accuracy of the feature extraction network for multi-scale targets;introducing the parameter-free attention mechanism SimAM in the feature fusion layer to enhance the model's feature extraction ability,and using the CARAFE upsampling method to enhance the network's receptive field and improve the perception of detailed features;integrating the ConvMixer Layer into the YOLOv5s network structure,the model's running speed was improved while maintaining detection accuracy and reducing the model's parameter count.The experimental re-sults show that the improved model has good detection performance in real samples,with a mean average precision of 97.1%,which is 4.9%higher than the original YOLOv5s model,and can effectively improve the issue of missed detection and false detection of floating objects on the water surface.关键词
漂浮物检测/YOLOv5s/多尺度特征检测/注意力机制/CARAFE/卷积混合层Key words
floating object detection/YOLOv5s/multi-scale feature detection/attention mechanism/CARAFE/ConvMixer Layer分类
建筑与水利引用本文复制引用
项新建,翁云龙,谢建立,郑永平,吴善宝,许宏辉,杨斌..基于改进YOLOv5s的水面漂浮物检测算法研究[J].人民黄河,2024,46(7):85-91,7.基金项目
浙江省自然科学基金资助项目(LQ16F030002) （LQ16F030002）
浙江省重点研发计划项目(202206) （202206）
杭州市科技计划发展项目(202203B21) （202203B21）
