基于屏蔽R-CNN的环形变压器主干网的铁路结构诊断

Yizhou Wang, Jierui Jiang, Y. Hong, Shenghao Gao, Minsi Hu, Chunmao Li, Mingyue Li, Dong Liu
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引用次数: 0

摘要

随着城市地铁建设的快速发展,铁路系统安全问题已成为地铁工程领域的热门话题之一。地铁系统运行时,铁路结构的构件很可能会发生漏气或断裂。到目前为止,关键部件的维护工作大多由人工完成,耗费了大量的时间和人力。CNN在物体检测上具有较高的精度,但对于铁路结构部件发生的轻微损伤可能无法定位。本文介绍了一种基于Swin变压器的铁路系统部件诊断方法,该方法可以对人眼难以分辨的微小目标物体进行检测和分割。性能最好的模型bbox mAP为0.546,segm mAP为0.443,可以部署在带摄像头的笔记本电脑上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Railway Structure Diagnosis Based on Swin-Transformer Backbone with Mask R-CNN
As the fast development of city metro construction, railway system safety has become one of the most popular discussions in metro engineering. The components of railway structure are likely to be air-slaked or breached while the metro system is running. Till now the essential components maintaining is mostly done by human, and this costs time and human resources. CNN has high accuracy on object detection but may not be able to locate the minor damage happened on railway structure components. In this study, a Swin Transformer based method is introduced for components diagnosis in railway system, which can detect and segment the target object, even they are sometimes small and indistinguishable for human eyes. The model with best performance reached the bbox mAP of 0.546 and segm mAP of 0.443, and it can be deployed on a laptop with camera.
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