Railway Track Identification and Extraction Based on Improved Convolutional Neural Network

Abstract

Objective Prevention and protection of foreign objects intrusion into railway becomes a hot research topic, and the division of intrusion areas requires detection of the track location. In order to determine the track location in the image, a method based on improved Mask R-CNN (Mask region convolutional neural network) combined with mathematical model is proposed to identify and extract the railway track. Method Firstly, Mask R-CNN is optimized and added with attention mechanism, and transfer learning is introduced into the above method to improve the model′s generalization ability. Then the optimized model is used to identify and segment the track, extracting the segmentation data and fitting with the corresponding mathematical expression to realize the identification and extraction of the track. The track in the image is divided into four categories and labeled with the Labelme labeling software, forming a training set and a test set. The training set is used to train the optimized model, and the test set to evaluate the detection results of the optimized model. Result & Conclusion The research results show that compared with other segmenting models and the original model, the proposed method performs well under the same training intensity, reaching 97.5% of the accuracy rate in track type determination, and basically above 80% in track segmentation. Tests show that the proposed method improves the general applicability of the detection by using the good performance of neural network, and can accurately determine the track type and segment the track.

Keywords

• railway track identification
• convolutional neural network
• deep learning