电子测量与仪器学报

魏德华,魏秀琨.基于ABI-RTLSeg的轨道线路多关键部件精细化检测及缺陷分析[J].电子测量与仪器学报,2025,39(8):79-90

基于ABI-RTLSeg的轨道线路多关键部件精细化检测及缺陷分析

Fine-grained detection and defect analysis for multiple key componentsin railway tracks based on ABI-RTLSeg

DOI：

英文关键词:multi-component inspection defect analysis instance segmentation ASPP morphological analysis ellipse fitting

基金项目:国家自然科学基金（52462047）、甘肃省自然科学基金（24JRRA277）项目资助

作者	单位
魏德华	1.兰州交通大学交通运输学院兰州730070；2.高原铁路运输智慧管控铁路行业重点实验室兰州730070
魏秀琨	北京交通大学交通运输学院北京100044

Author	Institution
Wei Dehua	1.School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China; 2.Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control, Lanzhou 730070, China
Wei Xiukun	School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

摘要点击次数: 666

全文下载次数: 630

中文摘要:

城轨轨道线路状态监测是确保铁路运输系统安全的关键任务之一。城轨轨道线路包括钢轨、扣件、螺栓和垫板等关键部件。针对实时精细化检测的需求，在前期工作的基础上，进一步研究并提出了一种基于实例分割的创新智能方法，用于快速精细化识别城市轨道交通线路的多关键部件，并分析量化常见缺陷的检测结果。在已有的RTLSeg模型基础上，融合感受野增强和图像后处理技术，提出了一种改进的轨道线路图像分割评估模型（ABI-RTLSeg）。首先，为了增强模型对高级语义信息的学习，在深层骨干网络中引入了空洞空间金字塔池化模块（ASPP）；其次，在Coord-Protonet中引入了基于卷积的双线性插值结构，以获得更高质量的原型掩模和语义信息感知；最后，根据缺陷分割掩模的视觉特征，构建了分割结果分析模块，综合运用椭圆拟合和形态学分析方法，分析常见缺陷的安全状态。实验结果表明，该方法在快速精细化检测、分割以及分析铁路轨道线路的多目标关键部件和常见缺陷方面是可行的，并且其性能优于比较的基线模型。ABI-RTLSeg在所构建的数据集上能够达到90.91%的边界框平均精度（bbox mAP）和91.67%的掩码平均精度（mask mAP）。同时，平均推理速度达到25.62 fps，平均检测准确率和召回率分别为100%和99.85%。此外，通过多个实例研究，探讨了所提方法在评估扣件损坏严重程度和估算轨道波磨关键参数方面的可行性，为城市轨道交通线路的安全状态监测提供了一种有效方案，为城市轨道交通的智能化发展提供了重要技术支撑。

英文摘要:

Urban rail transit track condition monitoring is one of the critical tasks for ensuring the safety of railway transportation systems. The urban rail transit track includes key components such as rails, fasteners, bolts, and sleepers. In response to the demand for real-time and refined detection, this study, building on previous work, further investigates and proposes an innovative intelligent method based on instance segmentation for the rapid and refined identification of multiple key components of urban rail transit tracks, analyzes, and quantifies the detection results of common defects. Specifically, this research, based on the existing RTLSeg model, integrates field-of-view enhancement and image post-processing techniques, proposing an improved track image segmentation and evaluation model (ABI-RTLSeg). Firstly, to enhance the model’s learning of high-level semantic information, this study introduces a dilated spatial pyramid pooling (ASPP) module into the deep backbone network. Secondly, a convolution-based bilinear interpolation structure is incorporated into the Coord-Protonet to obtain higher-quality prototype masks and semantic information awareness. Lastly, based on the visual features of defect segmentation masks, a segmentation result analysis module is constructed, employing ellipse fitting and morphological analysis methods to analyze the safety status of common defects. Experimental results demonstrate that this method is feasible for rapid and refined detection, segmentation, and analysis of multiple target key components and common defects of railway track lines, and its performance surpasses that of the comparative baseline models. In particular, ABI-RTLSeg is able to achieve 90.91% bbox mAP and 91.67% mask mAP with the customized dataset. Meanwhile, the average inference speed reaches 25.62 fps. The average detection accuracy and recall are 100% and 99.85%, respectively. Furthermore, the feasibility of the proposed methods for assessing the severity of fastener damage and estimating key parameters of rail corrugation has been explored through multiple case studies. In summary, this study provides a new technical approach for the intelligent monitoring of rail transit track lines, which is of great significance for improving the safety and reliability of the railway transportation system.

查看全文查看/发表评论下载PDF阅读器