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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).

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.