Pantograph catenary contact point is an important monitoring object during pantograph catenary operation, which reflects the state of pantograph catenary operation. However, due to the relatively small contact area of the target area, it is still a challenge to locate the contact point quickly and accurately. Therefore, we propose a two-stage detection method of rigid pantograph catenary contact points based on deep convolution neural network. Firstly, yolov3 network is used to locate the pantograph catenary contact part, which can obtain the target area including contact points. Then, four key points generated by the intersection of rigid pantograph and catenary can be obtained by using the key point detection network in the target area. Finally, the positioning of pantograph catenary contact points is obtained by geometric calculation. The experimental results on the railway operation data set collected by the traction Laboratory of Southwest Jiaotong University show the effectiveness of the method.

Monitoring train operation status is one of the most important tasks for ensuring rail operation safety. Pantograph and catenary (PAC) are collecting systems of the electric current from traction power supply system, and the stability of the contact between pantograph and catenary guarantees the stable power. However, most existing contact point (CPT) detection methods are always difficult to achieve precise positioning results, especially in complicated background. This article proposes a novel fast and accurate contact point detection method based on multiple strategies, which combine three modules. First, an improved kernelized correlation filter model in real-time tracking module was adopted to track the contact region. Then the pixel-level detection module was used to detect contact point in contact region via the proposed contact point regression residual network (CPRR-Net). Finally, a filter-based optimization module was added to correct the contact position using the Kalman filter. This work additionally employed a new rail dataset PAC-TPL2020 to prove the effectiveness and feasibility of the proposed multiple strategies in real-world scenarios, and the experimental results demonstrated the robustness and high accuracy (97.07% within 3 pixels and 99.97% within 5 pixels) of our model. It is noteworthy that our mothed runs at 65 frames per second for monitoring PAC contact points.

The pantograph is an important component of railway pantograph-catenary system, which can provide electric current for electrified railway electric locomotive. Since the pantograph is in an open roof environment, the defects of the pantograph are inevitable in the long run. In order to ensure the safe operation of trains, in this paper, we propose a new method for anomaly detection of pantograph based on salient segmentation and generative adversarial networks. First, an object location model is trained by U-Net which perform excellent properties for a small number of samples to accurately extract salient area of the pantograph. Second, a generative adversarial model is constructed to generate the reconstructed salient images of pantograph by vector mapping. Finally, the structural similarity algorithm is used to evaluate the similarity between the input salient image and the reconstructed salient image, so as to extract the image difference and realize the anomaly detection of the pantograph. Experimental results validate the effectiveness and accuracy of our approach.

With the proposal of intelligent high-speed railway, the research on remotely monitoring the intelligent traction substation becomes a key subject of high-speed railway safe operation. However, mosquitos adhered to the glass window or camera lens can severely hamper the visibility of a background scene, and degrade images considerably. Therefore, we propose a single image mosquito streaks removal method of high-speed railway traction substation based on the deep convolutional neural network. First, we employ guided filter to split input image into smoothing image and edge-preserving image. Then, the edge-preserving image is fed into our designed convolutional neural network to obtain learning map, which solves problems of background interference and focuses the model on the structure of rain streak in images, and the clean image is finally generated through adding the input image and learning map. The experiment results on Heishan traction substation real datasets show the effectiveness of our proposed method.

The pantograph catenary system is an important part of the traction power supply system. In order to monitor the dynamic parameters of the rigid catenary accurately in real-time, we propose a new coarse-to-fine approach to locate and detect the pantograph-catenary contact points using DCNNs. For the contact area, which is relatively small enough for the target region, we refer to a real-time object detection method with both speed and accuracy advantages, YOLOv3. And then based on the geometric relationship between the pantograph and the catenary, we use Hoff line detection to achieve accurate detection of contact points. Our method consists of two stages. We first train Yolov3 to detect the local region of the contact points accurately by using the pantograph-catenary datasets. Obtained images of the coarse region detection, we then choose the canny edge detection and Hough transformation to detection the pantograph-catenary contact points. The experiment results from two video datasets show that our proposed method can accurately track the pantograph-catenary contact points by the continuous detection, which could provide research refers to the real-time automatic monitoring of the pantograph-catenary system.

We propose a tracking method based on minimization of multi-detector trajectory entropy. Multiple detectors are constructed online to track objects synchronously by mutual information-based feature selection. Each detector obtains its own tracking trajectory through continuous detection. The tracking results of all detectors are combined based on the minimization of trajectory entropy, and the optimal detector is selected from it to determine the target trajectory and achieve the target tracking task. Experimental results show that our tracker can handle more complex tracking environments and outperform many other state-of-the-art methods in terms of both success rate and precision.