# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 1 Introduction  
A train operation control system is the basis of a safe and efficient operation of a railway, and train positioning technology is a key factor in operation control. Accurate and reliable positioning information is the premise of ensuring safety, efficiency and providing the best service [1, 2]. Traditional train positioning technology mainly includes track circuit, query/balise, and speed measurement positioning, as well as positioning based on odometer cumulative ranging. These positioning methods often have the problems of single structure, low positioning accuracy and error accumulation, which can not meet the increasingly complex system structure and high-speed running of trains [3, 4, 5]. With the continuous development of GPS, GLONASS and the Beidou satellite, navigation technology is applied to the field of train positioning to achieve high precision, real-time and strong continuous self-positioning of the train, which can greatly reduce the use of ground equipment, cost of construction and operation, and improve the efficiency of train operation [6]. However, positioning technology based on satellite navigation is inevitably affected by potential multipath errors in urban or sub-urban environments, such as ephemeris, satellite clock, ionosphere, tropospheric, and propagation delay errors, as well as inherent errors generated duringthe positioning process by receiver internal noise, which will affect the position estimation result [7]. In recent years, many scholars have proposed integrated positioning technology which combines the positioning method based on satellite navigation with other positioning methods, and greatly improves the positioning accuracy [8; 9; 10]. However, in the commonly used integrated positioning system, the problem of cumulative error still exists [11]. Therefore, after referring to the laying rules of the kilometer posts, some scholars propose the use of image recognition technology to correct the positioning system at a specific position, and use the absolute position information of the kilometer posts to eliminate the accumulated error, so as to achieve fixed-point correction [12; 13; 14]. The premise of using the absolute position information of kilometer posts is to detect and locate the kilometer post quickly and accurately. Then, the kilometer post can be identified to obtain the absolute position information. At present, the detection and location methods of kilometer posts mainly include the location method based on image edge, morphology, textural features and color division [15; 16; 17], as well as the combination method based on color and projection [12]. To sum up, the general kilometer post detection is implemented by using traditional methods. These methods are complex, time-consuming and inefficient, and they do not fully consider the influence of interference in the real environment such as different weather or trackside objects. The aim of this work is to optimize the kilometer post detection method, so as to improve the assisted positioning technology based on kilometer posts. In recent years, deep learning has achieved good application results in many research fields [18; 19], especially for license plate and road sign detection and recognition in the field of autonomous driving [20; 21]. At present, the deep learning algorithms that are commonly used in object detection can be divided into two categories--region- and regression-based detection algorithms. The region-based detection algorithm typically includes the pre-processing step of the candidate boxes hypothesis, and the whole detection process includes two stages. The potential object candidate boxes are assumed in the first stage, and then the convolutional features of the candidate regions are extracted. Finally, the candidate boxes are categorized by the classifier, and the locations are regressed [22]. The detection process of the regression-based detection algorithm is simple and fast [23]. It does not contain the candidate boxes hypothesis and the feature re-sampling stage.