计算机科学与探索2025，Vol.19Issue(6)：1598-1610,13.DOI:10.3778/j.issn.1673-9418.2404059
基于轻量化卷积和SCAM改进的X光违禁品检测
Improved X-ray Prohibited Items Detection Method Based on Lightweight Convo-lution Blocks and SCAM
摘要
Abstract
To resolve the problems of high overlap,occlusion,difficulty in extracting key feature information,and com-plex background interference in X-ray contraband images,an improved X-ray contraband detection model based on multi-branch lightweight convolution and attention mechanism is proposed.The proposed model designs the backbone network with the spatial-channel attention mechanism(SCAM),which distinguishes redundant and non-redundant information in feature maps by reorganizing them in the channel and spatial dimensions,enhancing the extraction of key features and suppressing background interference,and also improving the ability of the model to detect contraband in complex scenes.A multi-branch lightweight convolution structure(MLCB)is proposed,which uses a lightweight dual-branch and informa-tion compensation branch to process feature maps to reduce the number of model parameters,so as to improve the opera-tion efficiency.In addition,the minimum point distance intersection over union(MPDIoU)loss function and soft non-maximum suppression(Soft NMS)are integrated to replace the complete intersection over union(CIoU)loss function.By defining a more comprehensive intersection over union method,it alleviates the difficulty in optimization when bounding boxes overlap,and improves the problems of false positive and false negative caused by overlap of contraband.The pro-posed model is verified on OPIXray,HIXray and SIXray datasets,and mAP0.50 reaches 95.7%,83.7%and 95.3%,respec-tively.The results show that the proposed method has high accuracy and strong robustness with a small amount of calcula-tion,and it can solve the problems of overlap and occlusion as well as missed and false detections effectively.关键词
X光图像/违禁品检测/空间和通道重构/多分支轻量化卷积/损失函数Key words
X-ray images/contraband detection/spatial and channel reconstruction/multi-branch light convolution/loss function分类
计算机与自动化引用本文复制引用
左景,石洋宇,卢树华..基于轻量化卷积和SCAM改进的X光违禁品检测[J].计算机科学与探索,2025,19(6):1598-1610,13.基金项目
中国人民公安大学安全防范工程双一流创新研究专项(2023SYL08). This work was supported by the Double First-Class Innovation Research Project for People's Public Security University of China(2023SYL08). （2023SYL08）
