Anomaly Detection of Trackside Equipment Based on GPS and Image Matching

The health of trackside equipment often seriously affects the safe driving of the train, while manually checking their status is an indispensably laborious task. In order to automate the task, this paper addresses the problem of automatically and accurately detecting anomalies of equipment located along the track through GPS and image calibration techniques. Considering the unnoticeable changes in equipment, including screws missing and cable corrosion, detection by classification-based machine learning methods is difficult to implement, thus in this paper we propose and conduct a novel detection mechanism by an efficient way of image subtraction. Especially, our method consists of four steps. The first step is to collect images of the trackside equipment and their corresponding mileage in the same route several times through cameras and GPS devices installed in the inspection train. Then, by introducing an improved RANSAC algorithm, the GPS data is further corrected. Secondly, we define one pass of the collected image data along with GPS information as template, and for the rest passes that need to be detected, the first frame alignment operation is operated through GPS and image information. For the third step, the SURF algorithm is implemented for image matching, and then the subtraction operation is conducted between each matched image pairs. Finally, we use empirical filtering mechanism to remove false positives that we have detected. Experimental results demonstrate the efficiency and effectiveness of our proposed method for anomalies detection of trackside equipment.