High-precision real-time object detection model and benchmark for X-ray security inspection images

Image object detection technology has greatly improved the work efficiency of the security inspection and further guaranteed public security. However, the differences in imaging standards among different types of security inspection machines, the complexity of X-ray images, and the expensive cost of data annotation have constrained further research of object detection technology based on X-ray security inspection images. To improve the universality of our item detection system, we extend the dataset using a style transfer approach to account for variations in X-ray imaging hues of the same substance across various security equipment manufacturers. A refined feature pyramid network structure is proposed to extract richer semantic information from different levels in response to the significant differences in the size of similar objects to be recognized in X-ray images. A fine-grained classification module, which is simple to plug into the general object detectors, is what we suggest in order to increase detection accuracy even more. Meanwhile, this dataset contains 56659 X-ray images, featuring 37 types of contraband, with each image being high-quality annotated. This is a larger publicly available X-ray image dataset in terms of both the variety of contraband types and the number of images. Based on comparative experiments conducted on this X-ray contraband dataset, the model structure proposed in this article achieved an approximate 0.056 improvement in mean average precision (mAP) compared to the baseline model.