基于渐进式分类算法的刚性接触网螺母松动检测
张丞
Loosened Nut Detection in Rigid Catenary Systems Based on Progressive Classification Algorithm
ZHANG Cheng
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作者信息:中国铁道科学研究院集团有限公司城市轨道交通中心，100081，北京
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Affiliation:Urban Rail Transit Center，China Academy of Railway Sciences Group Co.， Ltd.， 100081， Beijing， China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.20252115
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中图分类号/CLCN:U225
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栏目/Col:供电与能源
摘要:
［目的］ 面对实际巡检环境中螺母状态多样、拍摄条件复杂的挑战，需实现刚性接触网螺母松动的自动化检测，以提高接触网巡检效率并降低人工成本。 ［方法］ 提出一种基于图像分割和三次渐进式串联分类的螺母松动检测方法：首先采用YOLOv8骨架网络对接触网图像进行分割，识别并定位螺栓螺母区域；然后构建参数共享的串联分类网络，实现净污螺母区分、有无防松线判别、防松线对齐状态的分类检测；最后通过参数共享机制，使得分类检测共用同一骨架网络提取特征，由此显著缩减计算量并提高检测速度。 ［结果及结论］ 采用中国铁道科学研究院的综合检测车车顶相机，拍摄多视角接触网图像数据作为测试集，在实际接触网图像数据集上进行上述方法的检测验证。结果表明，所提检测方法对松动螺母的查准率达86.73%，查全率达97.70%，满足工程应用需求。相较传统方法，通过所提检测方法建立的参数共享串联分类框架能高效、准确地检测出刚性接触网螺母松动位置，因此可为刚性接触网状态监测与维护提供技术支持。
Abstracts:
［Objective］ In response to the challenges posed by the diverse states and complex imaging conditions for nuts in real inspection environments， it is aimed to achieve automated detection of loosened nuts in rigid catenary systems， thereby improving inspection efficiency and reducing manual labor costs. ［Method］ A nut loosening detection method based on image segmentation and a three-stage progressive serial classification algorithm is proposed. First， the YOLOv8 backbone network is employed to segment catenary images， identify and locate bolt-nut regions. Then a parameter-sharing serial classification network is constructed to discriminate nut cleanliness， judge anti-loosening wire conditions， and the alignment status of these wires. The classification tasks share the same backbone network for feature extraction through a parameter-sharing mechanism， significantly reducing computational load and increasing detection speed. ［Result Conclusion］ Multi-angle catenary images captured by the roof camera of the comprehensive inspection vehicles from China Academy of Railway Sciences are used as the test dataset. The proposed method is tested and validated through actual catenary image datasets. The results show that the proposed method achieves a precision rate of 86.73% and a recall rate of 97.70% for detecting loosened nuts， meeting engineering application requirements. Compared with conventional methods， the parameter-sharing serial classification framework established by the proposed method can efficiently and accurately detect loosened nuts in rigid catenary systems， providing technical support for catenary condition monitoring and maintenance.
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