|Place of Conferral||北京|
|Keyword||高铁接触网 目标检测 目标跟踪 深度学习 场景文本识别|
In recent years, China High-speed Railway Network plays an important role in the industry structure upgrade, urbanization acceleration and regional economic interconnection. Meanwhile, with continuous operation of high-speed railway, we have accumulated massive video monitoring data, including the time and space data. It is essential for us to master the real-time state of the running train through the “Bow-Net” monitoring data analysis, which can greatly promote the high-speed railway intelligent management system. Among the whole data, Bow-Net system monitoring data is the main part. “Bow” refers to the pantograph, “Net” refers to the catenary. At present, general method of high-speed railway network video surveillance is conducted by model construction, integrating all the prior knowledge, digital image processing and computer vision technology into practical process, in order to achieve target detection and recognition, target tracking, and even posture analysis. However, developed target detection algorithms are limited by the design of artificial feature descriptors, it is difficult to rely on artificial design a feature descriptor with the characteristics of generality, robustness, and high-accuracy. Developed monitoring methods can be split into two groups—manual inspection and vehicle inspection. Manual inspection relies mainly on the workers experience level, although it has certain auxiliary equipment, but it has disadvantages such as time consuming and low efficiency, so it is suitable for special sections for small scale operation. Functional vehicle inspection device can hardly satisfy the requirement of real-time processing because of its expensive, less equipment, high maintenance costs, and its applicable period is very narrow in order to avoid conflicting with the running train. But even worse, due to captured images may appear unpredictable mutation and interference, traditional target detection methods can hardly satisfy the requirement of real-time processing，robustness, and high-accuracy detection.
In this paper we achieve online multi-target detection, tracking, and even scene text recognition of high-speed railway catenary parts, which plays an important role on High-speed Railway catenary intelligent monitoring system. First of all, we achieve the real-time, high precision detection and recognition of catenary locator based on deep convolution network, furthermore, we complete the locator slope precise measurement through skeleton outline fitting method under monocular vision framework. Second, we adopt the optimize model based on Kalman filter to improve cable and wire line recognition and tracking accuracy, in order to separate them when complicated backgrounds emerge, such as in tunnel, under bridge, and in station yard. Finally, in order to improve high-speed railway positioning accuracy, we design single character twice detection algorithm to achieve pole plate text recognition, and our method greatly improves the high-speed railway localization accuracy.
In this paper, our main work and contributions are as follows:
1. We present a robust and real-time, high precision of target detection and identification method and the application system, this solution method is successfully applied in the “Bow-Net” component detection and fault analysis system. We achieve tiny and feature-weak target detection based on deep convolution network. After positioning an outline of target precisely, we optimize the optimum fitting line segments based on Hough line transform, and we complete the locator slope non-contact precise measurement based monocular vision framework finally.
2. We propose novel detection methods of “Bow-Net” geometrical parameters and its component breakdown. The first part is that we put forward a collaborative tracking method based on Kalman filter, which realizes identification and tracking of the cable and wire lines of high-speed railway in complicated background and extreme environment. This method overcomes the birth defects of cable and wire lines captured in tunnel, which pixels saliency is not obvious enough. The second part is the fault detection of the catenary component. We use the target detection algorithm based on deep convolution network to realize the precise detection of catenary component breakdown.
3. We propose a real-time High-speed Railway positioning auxiliary methods, using computer vision technology, through deep convolution network for high-precision localization of pole plate, and then we achieve real-time pole plate number recognition by Scene Text Recognition algorithm and realize real-time positioning due to the pole plates conform to certain rules. Meanwhile, we adopt priori knowledge to refine the text recognition precision.
|First Author Affilication||Institute of Automation, Chinese Academy of Sciences|
|陈东杰. 高铁接触网在途检测与跟踪算法研究[D]. 北京. 中国科学院大学,2017.|
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|硕士毕业论文_陈东杰final.pdf（6140KB）||学位论文||暂不开放||CC BY-NC-SA||Application Full Text|
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