Author:
Liang Tianfen,Lv Bo,Zhang Nanfeng,Yuan Jinhao,Zhang Yanxi,Gao Xiangdong
Abstract
Abstract
Security inspection is an important measure to ensure public safety. At present, X-ray security inspection equipment is widely used in security checkpoints. However, it is not efficient to recognize prohibited items in X-ray images manually. Automated security inspection system has become the development trend of security field. In this paper, the Yolov4 detector was used to detect prohibited items in X-ray images. In order to improve the detection performance, we added CBAM attention modules to different parts of Yolov4. A public dataset is used for simulation experiments, which shows that the addition of CBAM can effectively improve the detection performance of the detector.
Subject
General Physics and Astronomy
Reference8 articles.
1. Modern computer vision techniques for x-ray testing in baggage inspection;Mery;IEEE Transactions on Systems, Man, and Cybernetics: Systems,2017
2. Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery;Akcay;IEEE Transactions on information forensics and security,2018
3. Multi-scale Prohibited Item Detection in X-ray Security Image;Zhang;Journal of Signal Processing,2020
4. Automatic detection of prohibited items with small size in X-ray images;Zhang;Optoelectronics Letters,2020
5. Yolov4: Optimal speed and accuracy of object detection;Bochkovskiy,2020
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献