Intrusion Detection of Foreign Objects in Railway Tracks Based on Hough and Camshift
1. Department of Electronic and Communication Engineering, School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian 116028, China
2. Department of Railway Signal and Control Engineering, School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian 116028, China
| Abstract: | With the rapid development of China's railways, the total amount of railway transportation has increased rapidly and the operating speed of railways has also been rapidly improved, which puts higher requirements on the reliability and safety of the entire railway network. Foreign object intrusion into railways poses a great threat to the reliability and safety of railway systems. In order to effectively avoid such phenomena, this article proposes a method for detecting foreign object intrusion in railway tracks based on the principle of image processing. Firstly, video data is obtained from the front of the train using the front camera of the train, and image processing techniques such as grayscale, median filtering, and edge processing are used to convert it into clear images to more accurately display the position of the railway tracks. The Otsu method is used for image threshold segmentation, and the Hough transform is used to remove residual impurities line and highlight the outline of the rail line. The background subtraction method in OpenCV library is used for detecting the moving targets, and the Camshift algorithm is used to track images, and determine whether suspicious objects have invaded the rail based on the degree of overlap between the tracked target and the rail. The GUI view provides the overlap ratio between the tracked target area and the rail area to determine whether the object has affected the driving. The effectiveness of the method proposed in this paper was verified through simulation. |
| Keywords: | Foreign Objects on Railway Tracks; Hough Transform; Otsu Method; Target Tracking; Camshift Algorithm |
| DOI: | 10.57237/j.jsts.2024.01.004 |
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Received: 1 January 2024
Accepted: 5 February 2024
Published Online: 11 March 2024
Funding
| 1. | 大连市重大科市重点科技局研发计划 (2022YF11GX008) |
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