计算机工程与应用2024，Vol.60Issue(10)：266-275,10.DOI:10.3778/j.issn.1002-8331.2308-0444
改进YOLOv7的X光图像危险品检测算法
Improved Dangerous Goods Detection in X-Ray Images of YOLOv7
摘要
Abstract
Aiming at the problems of complex background,serious occlusion and variable scale of X-ray security inspec-tion images in dangerous goods detection,the YOLOv7 algorithm is improved,which improves the detection accuracy and makes the network more lightweight.Firstly,the PS-ELAN module is built to replace the ELAN module in the origi-nal backbone network,which reduces the network computing amount and memory occupation,and improves the feature extraction capability of the network.Secondly,the parameter-free attention mechanism SimAM and deformable convolu-tional DCNv2 are fused into the downsampling stage of the neck network to improve the network's ability to capture the key features of dangerous goods in X-ray images.Finally,the Dynamic Head module is introduced to enhance the scale perception,spatial perception and task perception of the detection head,and improve the detection performance of the net-work.Experimental results show that the mean average precision(mAP)of the improved algorithm on the self-made data-set and CLCXray dataset is improved by 4.7 percentage points and 1.2 percentage points,respectively,and the number of parameters and calculations are reduced by 16.2%and 23.1%,respectively.The improved algorithm makes detection capa-bility lighter,which can play a good role in actual security checks.关键词
深度学习/X光安检图像/危险品检测/YOLOv7/注意力机制Key words
deep learning/X-ray security inspection image/dangerous goods/YOLOv7/attention mechanism分类
信息技术与安全科学引用本文复制引用
张继龙,赵军,李金龙..改进YOLOv7的X光图像危险品检测算法[J].计算机工程与应用,2024,60(10):266-275,10.基金项目
国家自然科学基金(51868037). （51868037）
