YOLO-STM:A network model for identifying prohibited items in X-ray security inspection images based on Swin-Transformer and MSDA

To ensure the smooth operation of rail transit,security screening has become an essential safeguarding measure. However,the identification of hazardous goods in most security screening images mainly relies on the subjective judgment of security inspectors,which is difficult to ensure its accuracy and timeliness. Therefore,this work studied on the deep learning-based algorithm for the recognition of dangerous goods in X-ray security inspection images,aiming to realize high-precision and intelligent detection of prohibited items. Firstly,the security inspection images containing nine kinds of prohibited items were collected in Chongqing rail transit stations. The dataset was augmented using an automatic color enhancement algorithm to enhance its diversity. The obtained dataset is referred as CRTXray. Then,addressing the issues of the poor identification performance of small-target dangerous goods and variation in imaging color and clarity across different security devices,an improved dangerous goods detection model-YOLO-STM based on Swin Transformer and Multi-scale Dilated Attention (MSDA) is proposed. Experimental results show that the YOLO-STM model achieves an average accuracy of 89.2%,and an average recognition accuracy of 91.8% at 50% IoU for all dangerous goods in the data set. Specifically,the model exhibits a recognition accuracy of 99.5% at 50% IoU for guns. Compared with the YOLO v8 model,the YOLO-STM model demonstrates improvements in average accuracy by 3.0% and average recognition accuracy by 4.0%.