江西建材Issue(9)：96-98,3.
基于YOLOv5s户外场景下混凝土裂缝识别模型改进
Enhancement of Concrete Crack Identification Model in Outdoor Environment Utilizing YOLOv5s
摘要
Abstract
In recent years,the development of machine vision algorithms has promoted the application of crack detection technology based on deep learning in concrete buildings.The technology uses Xiaomi 13 mobile phones and DJI drones to collect 3 134 images containing cracks,corrosion,and pits in natural environments to increase the generalization ability of the model.Improve the accuracy and efficiency of crack de-tection,especially in practical applications of non-fixed ends.To achieve this goal,the research introduced Mosaic-9 data enhancement and ECA and CA attention mechanisms to optimize the YOLOv5s model.Use the YOLOv5s target detection network and introduce the GhostNet network to optimize computational efficiency.The effectiveness of the lightweight optimization strategy is verified by an ablation experiment.The introduction of GhostNet increases the F1 score by 113.17%,the model recognition speed by 62.3%,and the recall rate by 23%,with a slight increase in model parameters and computation.The research shows that combining data enhancement,attention mechanism,and light-weight network optimization can effectively improve the accuracy and efficiency of concrete crack detection.关键词
混凝土裂缝/机器视觉/YOLOv5sKey words
Fissures in concrete/Machine vision technology/YOLOv5s分类
建筑与水利引用本文复制引用
邱锐,孙勇,解可悦,刘松林,尹萌,杨安琪,汤丰莹..基于YOLOv5s户外场景下混凝土裂缝识别模型改进[J].江西建材,2024,(9):96-98,3.基金项目
黑龙江省级大学生创新创业训练项目《基于机器视觉技术的混凝土外观检测与损伤识别》(项目编号:S202310219008). （项目编号:S202310219008）
