改进YOLOv7算法的排水管道缺陷检测与几何表征*

doi:10.16731/j.cnki.1671-3133.2024.03.015

现代制造工程 ›› 2024, Vol. 522 ›› Issue (3): 110-118.doi: 10.16731/j.cnki.1671-3133.2024.03.015

• 仪器仪表/检测/监控 • 上一篇下一篇

改进YOLOv7算法的排水管道缺陷检测与几何表征^*

曾飞^1,2, 李斌^1,2, 周健², 樊江峰²

1 武汉科技大学冶金装备及其控制教育部重点实验室,武汉 430081;
2 武汉科技大学机械自动化学院,武汉 430081

收稿日期:2023-06-02 出版日期:2024-03-18 发布日期:2024-05-31
作者简介:曾飞,博士,教授,主要研究方向为智能系统监测与控制。E-mail:zengfei2004@126.com;李斌,硕士研究生,主要研究方向为缺陷检测。E-mail:202203704223@wust.edu.cn
基金资助:
* 国家自然科学基金青年基金项目(61703215);交通教育研究会2021年教育研究课题项目(JTYB20-71);2020年湖北高校省级教学研究项目(2020341);2019年武汉科技大学教学研究项目(2019Z014)

Improved defect detection and geometric characterization of drainage pipes in YOLOv7 algorithm

ZENG Fei^1,2, LI Bin^1,2, ZHOU Jian², FAN Jiangfeng²

1 Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology,Wuhan 430081,China;
2 School of Machinery and Automation,Wuhan University of Science and Technology, Wuhan 430081,China

Received:2023-06-02 Online:2024-03-18 Published:2024-05-31

摘要/Abstract

摘要： 定期检查排水管道可以及时发现严重缺陷,对保证排水系统健康运行和城市环境安全具有重要意义。针对排水管道低照度和低分辨率检测困难现状,提出一种改进YOLOv7算法的排水管道缺陷检测与几何表征方法。首先,利用对比度受限自适应直方图均衡化图像增强技术,改善图像的对比度和细节,以提高检测网络对排水管道缺陷的捕获能力;其次,基于设计的Drop-CA和MC模块改进YOLOv7算法,使网络获得浅层缺陷的语义信息并降低误检率,提高模型的分类和定位能力;最后,针对裂缝和断裂2种严重缺陷,设计了一种定量描述该缺陷的几何特征方法来评估缺陷的大小。实验结果表明,改进的网络模型最终平均精度达到93.3 %,检测速度达到42.9 f/s。该方法有效提升排水管道缺陷检测和分类精度,且可以有效表征缺陷的几何特征。

关键词: 图像增强, 缺陷检测, 改进的YOLOv7算法, Drop-CA, 几何特征

Abstract: Regular inspection of drainage pipes can find serious defects in time,which is of great significance to ensure the healthy operation of the drainage system and the safety of the urban environment.Aiming at the difficulty of detecting low illumination and low resolution of lower drainage pipes,an improved drainage pipeline defect detection and geometric characterization method of YOLOv7 algorithm is proposed.Firstly,the Contrast-Limited Adaptive Histogram Equalization (CLAHE) image enhancement technique is used to improve the contrast and detail of the image,so as to improve the detection network′s ability to capture drainage pipe defects.Secondly,based on the design of Drop-CA and MC modules,the YOLOv7 algorithm is improved,so that the network can obtain the semantic information of shallow defects,reduce the false detection rate,and improve the classification and localization capabilities of the model.Finally,for the two serious defects of crack and fracture,a method is designed to quantitatively describe the geometric characteristics of the defect to evaluate the size of the defect.Experimental results show that the final average accuracy of the improved network model reaches 93.3 %,and the detection speed reaches 42.9 f/s. This method effectively improves the accuracy of defect detection and classification of drainage pipelines,and can effectively characterize the geometric characteristics of defects.

Key words: image enhancement, defect detection, YOLOv7 algorithm, Drop-CA, geometric features

中图分类号:

TP271

曾飞, 李斌, 周健, 樊江峰. 改进YOLOv7算法的排水管道缺陷检测与几何表征^*[J]. 现代制造工程, 2024, 522(3): 110-118.

ZENG Fei, LI Bin, ZHOU Jian, FAN Jiangfeng. Improved defect detection and geometric characterization of drainage pipes in YOLOv7 algorithm[J]. Modern Manufacturing Engineering, 2024, 522(3): 110-118.

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改进YOLOv7算法的排水管道缺陷检测与几何表征^*

Improved defect detection and geometric characterization of drainage pipes in YOLOv7 algorithm

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