X-YOLO: An Efficient Detection Network of Dangerous Objects in X-Ray Baggage Images

X-ray safety inspection machines are essential for maintaining security. In the past few years, the rapid development and widespread application of deep learning have significantly contributed to the progress of X-ray security detection. In order to achieve automatic detection of dangerous objects in X-ray baggage images, this paper proposes an efficient dangerous objects detection network called X-YOLO, which incorporates feature fusion and attention mechanisms specifically for X-ray baggage images. In the proposed network, an innovative attention mechanism module is integrated into the architecture, and an improved Dynamic Head module is designed to enhance object detection across various dimensions. In the experiments, the HiXray and OPIXray datasets are selected, and the experimental results show that the X-YOLO network demonstrates excellent performance when compared with several state-of-the-art networks. The mAP50 value reached 86.6% on the HiXray dataset and 93.7% on the OPIXray dataset, representing improvements of 2.4% on the HiXray dataset and 4.6% on the OPIXray dataset compared with the baseline.