北京航空航天大学学报2026，Vol.52Issue(2)：533-540,8.DOI:10.13700/j.bh.1001-5965.2024.0459
X射线安检图像高精度实时目标检测模型与基准数据集
High-precision real-time object detection model and benchmark for X-ray security inspection images
支洪平 1孙立峰 1王旭2
作者信息
- 1. 清华大学 计算机科学与技术系,北京 100084
- 2. 科大讯飞(苏州)科技有限公司,苏州 215000
- 折叠
摘要
Abstract
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 56 659 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.关键词
X射线安检图像/风格迁移/目标检测/细粒度分类模块/基准数据集Key words
X-ray security inspection images/style transfer/object detection/fine-grained classification module/benchmark分类
信息技术与安全科学引用本文复制引用
支洪平,孙立峰,王旭..X射线安检图像高精度实时目标检测模型与基准数据集[J].北京航空航天大学学报,2026,52(2):533-540,8.
