Track Limit Detection Algorithm of Fully Automatic Train Based on Vision Sensor

With the benefits of a wide monitoring range, easy installation and maintenance, as well as good remarkable observability, vision sensors are gradually being applied to engineering measurement and environmental perception research in rail transportation. A sliding pane-like track limit detection algorithm based on vision sensor is proposed for the track limit detection in the field of obstacle detection for fully automatic driverless trains in rail transit. The algorithm uses the two-stage inter-frame differential thresholding method to detect the motion of video frames, and uses grayscale distribution feature extraction and adaptive Sobel operator thresholding edge detection methods to improve the accuracy and integrity of the image scene recognition and edge detection. The track limit was extracted by the tack limit search module based on the sliding pane, and Kalman filter is used to improve the accuracy and robustness of the detection results. The experimental validation results show that the algorithm has good detection performance on track limit.