Object Detection and X-Ray Security Imaging: A Survey
Jiajie Wu, Xianghua Xu, Junyan Yang
Abstract
Security is paramount in public places such as subways, airports, and train stations, where security screeners use X-ray imaging technology to check passengers’ luggage for potential threats. To streamline this process and make it more efficient, researchers have turned to object detection techniques with the help of deep learning. While some progress has been made, there are few comprehensive literature reviews. This paper provides a comprehensive overview of the standard object detection algorithms and principles in X-ray dangerous goods detection. The article begins by classifying and describing the more popular deep learning object detection techniques in detail and presenting the commonly used publicly available datasets and metrics. And then go on to summarize previous applications of deep learning techniques in X-ray dangerous goods detection, highlighting their successes and limitations. Finally, based on an analysis of the experimental results, it summarizes some of the limitations of deep learning in X-ray baggage detection thus far. It offers insights into the future of this exciting field. With this review, we hope to provide valuable insights and guidance for those seeking to improve public safety through X-ray imaging and deep learning technology.