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Visual Recognition System of Elastic Rail Clips for Mass Rapid Transit Systems

机译:质量快速交通系统弹性轨夹的视觉识别系统

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In recent years, the railway transportation system has become one of the main means of transportation. Therefore, driving safety is of great importance. However, because of the potential of multiple breaks of elastic rail clips in a fixed rail, accidents may occur when a train passes through the track. This paper presents the development of a computer visual recognition system which can detect the status of elastic rail clips. This visual recognition system can be used in mass rapid transit systems to reduce the substantial need of manpower for checking elastic rail clips at present. The visual recognition system under current development includes five components: preprocessing, identification of rail position, search of elastic rail clip regions, selection of the elastic rail clip, and recognition of the elastic rail clip. The preprocessing system transforms the colored images into grey-level images and eliminates noises. The identification of rail position system uses characteristics of the grey-level variation and confirms the rail position. The search system uses wavelet transformation to carry out the search of elastic rail clip regions. The selection system finds a suitable threshold, using techniques from morphological processing, object search and image processing. The recognition system processes characteristics and structures of elastic rail clips. Experimental testing shows the ability of the developed system to recognize both normal elastic rail clip images and broken elastic rail clip images. This result confirms the feasibility in developing such a visual recognition system.
机译:近年来,铁路运输系统已成为主要交通工具之一。因此,驾驶安全性非常重要。然而,由于在固定轨道中多次破坏弹性轨夹的电位,当火车通过轨道时可能发生事故。本文介绍了计算机可视识别系统的开发,可以检测弹性轨夹的状态。该视觉识别系统可用于质量快速传输系统,以减少目前检查弹性轨夹的人力的实质性需求。当前开发下的视觉识别系统包括五个组件:预处理,轨道位置的识别,弹性轨夹区域的搜索,选择弹性轨道夹,以及弹性轨道夹的识别。预处理系统将彩色图像转换为灰度级图像并消除噪声。轨道位置系统的识别使用灰度级变化的特性并确认轨道位置。搜索系统使用小波变换来执行弹性轨夹区域的搜索。选择系统使用来自形态处理,对象搜索和图像处理的技术找到合适的阈值。识别系统处理弹性轨夹的特性和结构。实验测试显示了开发系统识别正常弹性轨剪辑图像和破碎的弹性轨剪辑图像的能力。该结果证实了开发这种视觉识别系统的可行性。

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