A deep learning method for X-ray image safety detection: YOLO-T

Abstract

There are many problems in X-ray image dangerous goods target recognition with existing technology, such as low degree of automation, slow detection time, easy to misjudge under occlusion interference, etc. Based on the above problems, this paper proposes a multi-objective intelligent security inspection method for X-ray images based on the YOLO-T deep learning network. By adding the optimized Transformer structure to the YOLO architecture, this method can better solve the above problems. In order to better carry out the experiment, we proposed a set of X-ray safety detection data set GDXray-Expanded containing multiple categories of dangerous goods, and tested several versions of the deep learning network model of the YOLO series on this basis. Experiments show that the existing YOLO series algorithms still cannot solve the problem that dangerous goods in X-ray images are easy to be misjudged under occlusion interference. The YOLO-T method proposed in this paper solves this problem well, and in the big data set test, the maximum mAP can reach 97.73%, which is 7.66%, 16.47%, and 7.11% higher than the three methods of YOLO v2, YOLO v3, and YOLO v4 respectively, and has achieved the most competitive performance in the detection of seven categories of dangerous goods. To sum up, the YOLO-T network proposed in this paper mainly solves a series of problems in the field of dangerous goods target recognition and detection in X-ray security inspection images and has a high engineering application prospect in the field of X-ray security inspection.