Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-occlusion Attention Module
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Object detection has taken advantage of the advances in deep convolutional networks to bring about significant progress in recent years. Though promising results have been achieved in a multitude of situations, like pedestrian detection, autonomous driving, etc, the detection of prohibited items in X-ray images for security inspection has received less attention. Meanwhile, security inspection often deals with baggage or suitcase where objects are randomly stacked and heavily overlapped with each other, resulting in unsatisfactory performance in detecting prohibited items in X-ray images due to the variety in scale, viewpoint, and style in these images. In this work, first, we propose an attention mechanism named De-occlusion attention module (DOAM), to deal with the problem of detecting prohibited items with some parts occluded in X-ray images. DOAM hybrids two attention sub-modules, EAM and RAM, focusing on different information respectively. Second, we present a well-directed dataset, of which the images in the dataset are annotated manually by the professional inspectors from Beijing Capital International Airport. Our dataset, named Occluded Prohibited Items X-ray (OPIXray), which focuses on the detection of occluded prohibited items in X-ray images, consists of 8885 X-ray images of 5 categories of cutters, considering the fact that cutters are the most common prohibited items. We evaluate our method on the OPIXray dataset and compare it to several baselines, including popular methods for detection and attention mechanisms. As compared to these baselines, our method enjoys a better ability to detect the objects we desire. We also verify the ability of our method to deal with the occlusion by dividing the testing set into three subsets according to different occlusion levels and the result shows that our method achieves a better performance with a higher level of occlusion.
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