Foreign Object Detection between PSDs and Metro Doors Using Deep Neural Networks

At present, many cities have established sophisticated metro transportation systems, and metro passenger traffic accounts for about 50 to 80 percent of the total passenger traffic. Unfortunately, there are hundreds of incidents of train delays and even casualties caused by foreign objects in metros of various cities every year. The detection of foreign objects between metro doors and Platform Screen Doors (PSDs) has become an urgent problem to be solved. The main contribution of this paper is that this is the first attempt to use deep neural networks to solve the problem of foreign object detection between the PSDs and metro doors. In this paper, we first introduce the object detection framework called You Only Look Once (YOLO series) and Single Shot MultiBox Detector (SSD). We further explore the performance of YOLO v3 and SSD by implementing their models to do the foreign object detecting task in our dataset (from real-world data). The experimental results demonstrate that deep neural networks achieve good detection accuracy in the task of foreign object detection between the PSDs and metro doors. In addition, the deep neural network can also give the category information of foreign objects, which greatly reducing the time of artificial judgment and relieving traffic pressure. We hope our paper will serve as a solid baseline and help ease future research in foreign object detection between the PSDs and metro doors.