摘要
为确保国家公共安全,有效防范违禁品可能引发的安全隐患,X光安检违禁物品检测技术作为维护公共安全的关键手段之一,其检测准确性与检测效率的提升具有重要意义。对基于深度学习的X光安检违禁物品检测技术进行了探索,重点研究了RT-DETR模型算法,并且在原模型基础上添加RC_EMA和DySample来改进R T-DETR模型,最后选取OPIXray数据集来进行实验验证。通过消融实验验证了改进后的模型在保持计算效率的同时,精确度、召回率和mAP@0.5指标都有显著提升。指标的提升不仅展示了深度学习在提高公共安全方面的潜力,而且为X光安检违禁物品检测技术的优化提供了有益的参考。
To ensure national public safety and effectively prevent security risks caused by prohibited items,X-ray security inspection technology for prohibited items detection,as one of the key measures to maintain public safety,holds significant importance in improving detection accuracy and efficiency.X-ray security inspection based on deep learning has been primarily focused,with special attention given to the RT-DETR algorithm.To improve the performance of RT-DETR model,RC_EMA and DySample are integrated into the original model.The OPIXray dataset is selected for experimental validation.Ablation experiments verify that the refined model achieves significant improvements in precision,recall rate,and mAP@0.5 metrics while maintaining computational efficiency.These improvements of indicators not only demonstrate the potential of deep learning in enhancing public safety,but also provide valuable references for the optimization of X-ray security inspection technology for prohibited item detection.
作者
刘卫华
王蓉
胡配雨
王建华
陈向南
陈壮
LIU Weihua;WANG Rong;HU Peiyu;WANG Jianhua;CHEN Xiangnan;CHEN Zhuang(School of Artificial Intelligence,Gansu University of Political Science and Law,Lanzhou 730070,China;Lanzhou Railway Public Security Bureau,Lanzhou 730014,China)
出处
《中国人民公安大学学报(自然科学版)》
2025年第2期59-67,共9页
Journal of People’s Public Security University of China(Science and Technology)
基金
甘肃政法大学科研资助项目(GZF2021XQN15)
甘肃省教育厅高校教师创新基金项目(2023A-100)。
