Design and Performance Evaluation of an AI-Based W-Band Suspicious Object Detection System for Moving Persons in the IoT Paradigm

Abstract

The threat of terrorism has spread all over the world, and the situation has become grave. Suspicious object detection in the Internet of Things (IoT) is an effective way to respond to global terrorist attacks. The traditional solution requires performing security checks one by one at the entrance of each gate, resulting in bottlenecks and crowding. In the IoT paradigm, it is necessary to be able to perform suspicious object detection on moving people. Artificial intelligence (AI) and millimeter-wave imaging are advanced technologies in the global security field. However, suspicious object detection for moving persons in the IoT, which requires the integration of many different imaging technologies, is still a challenge in both academia and industry. Furthermore, increasing the recognition rate of suspicious objects and controlling network congestion are two main issues for such a suspicious object detection system. In this paper, an AI-based W-band suspicious object detection system for moving persons in the IoT paradigm is designed and implemented. In this system, we establish a suspicious object database to support AI technology for improving the probability of identifying suspicious objects. Moreover, we propose an efficient transmission mechanism to reduce system network congestion since a massive amount of data will be generated by 4K cameras during real-time monitoring. The evaluation results indicate that the advantages and efficiency of the proposed scheme are significant.