Automatic online vision-based inspection system of coupler yoke for freight trains

Fault inspection plays an important role in ensuring the safe operation of freight trains. With the development of computer vision technology, the vision-based fault inspection has become one of the principal means of fault inspection. A coupler yoke is an important component of the train's connection system, and faults in this system would cause the separation of the train, leading to a serious accident. We propose an automatic image inspection system to inspect for faults in coupler yokes during the running of a freight train. The inspection process is divided into two parts: the localization part and the recognition part. In the localization part, we propose multiple dimension features, design a fast algorithm to compute multiresolution image features, and use a linear support vector machine classifier to locate the position of the coupler yoke. In the recognition part, we propose a fast decision tree training method by prepruning noneffective features, and use Adaboost decision trees as the final fault classifier. Experimental results show that this proposed method can achieve a fault inspection rate of 98.6% while the average processing time of an image is about 98 ms, which shows our system has a high inspection accuracy and a good real-time performance.