Prohibited Object Detection in X-ray Images with Dynamic Deformable Convolution and Adaptive IoU

Due to the variety and complexity of objects in X-ray images, how to detect the prohibited items automatically and accurately is a challenging problem. In this paper, an X-ray image prohibited object detection method based on Dynamic Deformable Convolution (DyDC) and adaptive Intersection over Union (IoU) is proposed based on Cascade R-CNN framework. The main contributions are as follows. First, DyDC is proposed to cope with the diversity of the prohibited objects in X-ray images and to improve the feature representation capability. Then, adaptive IoU mechanism is proposed, which can dynamically adjust the IoU threshold during the training process to generate high quality proposals. The proposed method is extensively evaluated on two publicly available benchmark datasets, namely SIXray and OPIXray, and the experimental results show that it can achieve the state-of-the-art detection accuracy, compared with other existing methods.