基于YOLOv5算法的长江大保护水利工程项目多场景质量安全检测  

Multi-scenario quality and safety inspection of Yangtze River protection water conservancy projects based on YOLOv5 algorithm

作　　者：徐亮 陈旭[2] 张卓 郑向泉 XU Liang;CHEN Xu;ZHANG Zhuo;ZHENG Xiangquan(Yangtze Ecology and Environment Company Limited,Wuhan 430014,China;Business School,Hohai University,Nanjing 211100,China)

机构地区：[1]长江生态环保集团有限公司,湖北武汉430014 [2]河海大学商学院,江苏南京211100

出　　处：《水利水电科技进展》2025年第2期82-89,共8页Advances in Science and Technology of Water Resources

基　　金：长江生态环保集团有限公司科研项目(HBZB2022005);江苏省社会科学基金项目(23GLD006);南京工程学院引进人才科研启动基金项目(YKJ202321)。

摘　　要：为解决长江大保护水利工程项目施工中质量安全隐患检测效率低、主观性强、易漏检等问题,通过分析项目多场景质量安全检测任务需求,明确了各类质量安全隐患的具体场景,利用YOLOv5算法进行了图像增强优化并搭建了智能识别算法架构,采用现场拍摄、网络爬虫技术及项目部内部数据资源,搜集并整理了上千张高质量照片,构建了质量安全图像数据集。在此基础上,通过融入区域检测功能,多场景质量安全检测系统能对指定的作业区域进行精准监测,可以有效地避免误检情况,提升检测效率与准确性。To solve the problems of low efficiency,strong subjectivity,and prone to missed inspection in quality and safety hazards inspection during the construction of the Yangtze River protection water conservancy projects,the specific scenarios of various quality and safety hazards were identified by analyzing the requirements of multi-scenario quality and safety inspection of the projects.The YOLOv5 algorithm was used for image enhancement and optimization,and an intelligent recognition algorithm framework was established.Through on-site photography,web crawling technology,and internal data resources of the project department,thousands of high-quality photos were collected and organized to obtain a comprehensive dataset.On this basis,by integrating the regional inspection functions,the quality and safety inspection system can accurately monitor only the designated work areas.This effectively avoids false inspection and improves inspection efficiency and accuracy.

关 键 词：长江大保护 水利工程项目 质量安全检测 图像增强 多场景 YOLOv5算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]