铁路道岔场景识别与间距检测

To solve the problems of low efficiency and poor accuracy of current railway turnout manual detection methods, a method based on deep learning to quickly identify turnout scene and detect turnout spacing was proposed. The railway point cloud information was obtained by linear array industrial camera scanning, the railway turnout scene recognition network connected by residual was designed > the optimal super parameters were searched with Tree-structured Parzen Estimator (TPE) algorithm, and the unbalanced number of samples was solved by focal loss function, so as to realize the accurate and fast recognition of railway turnout scene. Based on the recognized scene image of railway turnout, an edge extraction algorithm of turnout stock rail and switch rail was developed to accurately measure the inner distance between turnout stock rail and switch rail. The experimental results showed that the recognition accuracy of the proposed method reached 97.5 %, the recognition time was within 0.02s, and the calculation error of turnout spacing was less than 0.2mm. Compared with the manual detection method, the detection efficiency and accuracy were greatly improved, which met the requirements of turnout detection.