The automatic detection of foreign objects between platform screen doors (PSDs) and metro train doors significantly affects personnel and property safety and maintains the train’s normal operation. However, some existing works only determine the presence of foreign objects but cannot indicate their categories. Besides, although deep-learning-based object detection algorithms can indicate the presence and categories of foreign objects, most of them only harness the information in region proposals, ignoring global contextual information. Furthermore, their performance comes at the considerable cost of computational complexity, and leading cannot be well deployed in the metro environment. To address these issues and better implement foreign object detection (FOD), we present You Only Look Once-Transformer (YOLO-Former), a simple but efficient model. YOLO-Former is accomplished based on YOLOv5 through the following procedure. First, the vision transformer (ViT) is introduced for dynamic attention and global modeling, thereby solving the problem that the original YOLOv5 only utilizes information in region proposals and has insufficient ability to capture global information. Second, the convolutional block attention module (CBAM) and Stem module are used to improve feature expression ability further and reduce floating point operations (FLOPs). Finally, we design various variants with different widths and depths to meet every need. Experiments on the foreign object detection dataset (FODD) and PASCAL VOC dataset demonstrate that YOLO-Former-x consistently outperforms other state-of-the-arts with significant margins (0.5 to 11.3 mean average precision, mAP, on FODD and 0.6 to 13.6 on PASCAL VOC dataset). Last but not least, YOLO-Former-x maintains real-time processing speed (27.32 and 28.17 frame per second, FPS, on TITAN Xp).

Platform Screen Doors (PSDs) have been widely used in modern Asian and European metro systems due to the advantages of safety, comfort for passengers. Unfortunately, someone or something will be caught by PSDs and metro doors occasionally, which may lead to serious accidents. Therefore, the foreign object detection between PSDs and metro doors is a burning problem. Moreover, this problem is a challenging and still largely under-explored topic. In recent years, we have seen significant improvements in generic object detection built on deep learning techniques. Accordingly, this paper adopts deep learning technologies to address the problem of foreign object detection between PSDs and metro doors. To the best of our knowledge, this is the first attempt to use deep learning to solve the problem. To realize this, a dataset including 984 real-world images (with 600 × 480 pixels) labeled for six types of foreign objects (bag, bottle, person, plastic bag, umbrella, other) is developed. Then, we compared the performance of some state-of-the-art object detection algorithms (such as You Only Look Once -YOLOv3, Single Shot MultiBox Detector -SSD, and CenterNet) on the dataset. Experimental results demonstrate that the foreign object detection algorithms based on deep neural networks have achieved excellent results, which not only improves the accuracy of detection but also give the categories of foreign objects. YOLOv3 - tiny can achieve the fastest detection speed, up to 200 Frame Per Second (FPS); CenterNet can achieve the best detection results, up to 99.7% mean Average Precision (mAP).

The intrusion of foreign objects in tracks, one of the sources of injuries and fatalities in metro stations, can be solved as an anomaly detection task. However, the existing anomaly detection methods rarely consider the importance of updating the new knowledge from false alarm data, resulting in repeated mistakes. These methods are also impractical for edge devices that cannot afford the high calculation cost. A sequential updatable anomaly detection (SUAD) framework is proposed to tackle these problems. This framework is based on the Robbin-Monro algorithm and a fast version of Mahalanobis distance. A well-trained model of SUAD can continue to learn new knowledge through the sequential knowledge update module based on the Robbin-Monro algorithm without reviewing the old data. SUAD utilizes a new Mahalanobis distance calculation method based on principal component analysis. This new method exhibits a fast inference speed with a lighter model size than before. SUAD is evaluated on a self-built Metro Anomaly Detection (MAD) dataset and three public datasets. SUAD achieves an average area under the receiver operating characteristic curve score of 99.4% at image level and 99.6% at pixel level on MAD. SUAD also reduces at least 78% model size and 60% memory usage. Competitive results are also achieved in public datasets, including MVTec AD, beanTech Anomaly Detection, and CIFAR-10.