Detection and Identification of Hazardous Hidden Objects in Images: A Comprehensive Review

Abstract

Hidden object detection has attracted a lot of attention recently due to its importance in security surveillance and other real-world applications. It is considered one of the most challenging tasks in computer vision. Thanks to deep learning for playing a significant role in the rapid technical evolution in this field over the past decade. This article presents a roadmap of hidden object detection, starting from its insightful evolution in 1984, and extensively reviews the technical evolution and shifts in detection approaches. To the best of our knowledge, this is the first ever review work carried out in this field. Various aspects related to hidden object detection have been discussed, including basic building blocks of the detection system, historical milestone detectors, detection datasets, challenges, pre-processing techniques, modern state-of-the-art detection frameworks, and the various evaluation metrics used to assess the detection performance. Towards the end, the paper emphasizes on some unanswered research concerns and possible future prospects in the field of hidden object detection. This review paper aims to serve as a valuable resource for researchers, practitioners, and enthusiasts seeking a thorough understanding of the concepts, advancements, and challenges in this dynamic area of computer vision as hidden object detection continues to have an impact on a variety of interdisciplinary fields of research.