地理空间信息2026，Vol.24Issue(4)：22-27,6.DOI:10.3969/j.issn.1672-4623.2026.04.005
一种基于改进YOLO网络的混凝土裂缝识别方法研究
Research on Concrete Crack Identification Method Based on Improved YOLO Network
摘要
Abstract
In order to improve the accuracy and real-time performance of concrete crack detection,according to the problems of uneven sample distribution and difficulty in identifying small cracks in traditional methods,we proposed an improved YOLO deep neural network.To solve the issue of uneven sample distribution,we adopted data augmentation strategies and introduced the SlideLoss function,which could effectively alle-viate training difficulties caused by class imbalance.Meanwhile,by integrating the LSKNet attention mechanism,we significantly enhanced the model's ability to capture features of tiny cracks.In addition,the original backbone network of YOLOv8 was replaced with the lightweight MobileNetV3,which could improve the accuracy and efficiency of crack identification while reducing computational load and memory usage.Com-pared with the original YOLOv8 network,the improved network achieves increases of 4.2%,3.8%,and 5.5%in recall,precision,and mAP@0.5,respectively.Experimental results show that the proposed improved model achieves high detection accuracy and significantly accelerated running speed in concrete crack identification tasks,providing an efficient and practical crack detection technology for practical engineering applications.关键词
混凝土裂缝识别/YOLO深度神经网络/MobileNetV3/全局注意力机制Key words
concrete crack identification/YOLO deep neural network/MobileNetV3/global attention mechanism分类
信息技术与安全科学引用本文复制引用
罗洪波,马祥元,万雷,余凡..一种基于改进YOLO网络的混凝土裂缝识别方法研究[J].地理空间信息,2026,24(4):22-27,6.基金项目
国家重点研发计划资助项目(2021YFE0194700) （2021YFE0194700）
自然资源部数字制图与国土信息应用重点实验室开放研究基金资助项目(ZRZYBWD202301) （ZRZYBWD202301）
湖北省水利信息感知与大数据工程技术研究中心自主创新项目(CX2023Z07-2) （CX2023Z07-2）
流域水安全保障湖北省重点实验室自主创新项目(CX2023Z04-5). （CX2023Z04-5）
