In recent years, terrorist attacks have been spreading worldwide and become a public hazard to human society. The suspicious object detection system is an effective way to prevent terrorist attacks in public places. However, traditional systems face two main challenges: First, they need to conduct security checks at the entrance one by one, which leads to crowding; second, they rely heavily on screeners' ability to understand security images, which can easily lead to misjudgment. To address these issues, we propose an AI-based W-band suspicious object detection system for moving persons that can perform a two-stage walkthrough screening for suspicious objects in an open area to maintain high throughput. The 1st screening uses millimeter wave radar and cameras to automatically screen suspects who may have concealed suspicious objects in an open area. The 2nd screening involves security personnel using a hybrid imager with active and passive imaging capabilities to identify the specific suspicious objects carried by the suspect. Convolutional neural network (CNN) based artificial intelligence (AI) technology will be used to improve the accuracy and speed of suspicious object detection. We performed an experiment to validate the proposed system. The usability and safety of the system are demonstrated by recognition rate (aka accuracy rate) or both recall and precision rate. In addition, in the process of improving the suspicious object recognition rate by AI techniques, we use generative adversarial network to help build a suspicious object database and successfully validate the effectiveness of the method and the factors affecting the suspicious object recognition rate to optimize the system.
ASJC Scopus subject areas
- コンピュータ ネットワークおよび通信