# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 1 Introduction
All the computation is encapsulated in a unified network, and the results of object detection and location regression can be obtained directly by the forward operation. The optimization training can be carried out in an end-to-end manner. The commonly used region-based detection algorithms are Region-Based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN [24] and so on. Although the detection speed of this kind of algorithm is greatly increased, it is still slow and cannot meet the real-time requirements. The regression-based detection algorithm mainly includes a series of You Only Look Once (YOLO) [25] algorithms and an SSD [26] algorithm. The YOLO algorithm is a further improvement on R-CNN, with a faster detection speed; however, the detection accuracy is reduced. The SSD algorithm places multiple default boxes with different aspect ratios in each pixel position of the feature map with different resolutions, and uses the same regression as that of YOLO to regress the feature maps with different receptive fields. Therefore, SSD not only ensures the detection speed, but also achieves a detection accuracy comparable to that of Faster R-CNN [27]. Therefore, among all the algorithms, the SSD algorithm achieves a good balance between detection speed and accuracy, making it more suitable for practical application [28]. Furthermore, many scholars have optimized SSD algorithms and proposed a more general object detection network [29; 30; 31]. In reference [31], an improved SSD detection model named ZRNet was proposed. The experimental results show that it is better than other commonly used deep learning detection networks. Both the detection accuracy and efficiency are improved. Therefore, in this paper, a kilometer post detection model based on an improved SSD algorithm is established. First of all, the superiority of the model was ensured by inheriting the optimization ideas of reference [31]. Then, because the detection object in this paper is only the kilometer post, that is, compared with [31], the detection object was single and there was no overlapping phenomenon. Therefore, in order to improve the detection efficiency, the model was further simplified. We deleted the complex Z-style structure and residual network,and only extracted the backbone network of ZRNet for modeling. In addition, we also reduced the number of anchor boxes from 4 to 2. Furthermore, the establishment of a detection model based on an improved SSD algorithm needs a large number of kilometer post data for training and validation, but the data are not easy to obtain. There are many methods for sample expansion, which are mainly divided into the traditional method and Generative Adversarial Networks (GAN) [32]. The traditional methods mainly include rotation transform, flip transform, scale transform, gray-scale transform, noise disturbance and combination transform [33]. Although these methods can achieve sample expansion, the number of generated samples is limited, and the generated samples contain fewer features. As an improved model of GAN, DCGAN [34; 35; 36] has strong feature extraction ability and the generated samples are closer to the real samples. Therefore, this paper first introduces the DCGAN generation algorithm for sample expansion, and then realizes the data set. As a summary, the key contributions and innovations of our study can be summarized as follows. Firstly, by introducing the DCGAN generation algorithm, the effective data set of kilometer posts was constructed based on a small amount of real data. This not only greatly reduced the cost of real data acquisition, but also provided a prerequisite for the construction of a kilometer post detection model. Secondly, the superiority of the model was ensured by using the optimization methods in reference [31]. Furthermore, through the analysis of specific application scenarios, the model was further simplified.