Open AccessProhibited Items Detection in X-ray Images Based on Attention Mechanism
Author(s) -
Tianfen Liang,
Bo Lv,
Nanfeng Zhang,
Jinhao Yuan,
Yanxi Zhang,
Xiangdong Gao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1986/1/012087
Subject(s) - detector , public security , computer science , field (mathematics) , computer security , measure (data warehouse) , artificial intelligence , computer vision , data mining , telecommunications , mathematics , public administration , political science , pure mathematics
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.