Visual inspection for illicit items in X-ray images using Deep Learning

Abstract

Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage object detectors. However, no comparative experimental assessment of the various relevant DNN components/methods has been performed under a common evaluation protocol, which means that reliable cross-method comparisons are missing. This paper presents exactly such a comparative assessment, utilizing a public relevant dataset and a well-defined methodology for selecting the specific DNN components/modules that are being evaluated. The results indicate the superiority of Transformer detectors, the obsolete nature of auxiliary neural modules that have been developed in the past few years for security applications and the efficiency of the CSP-DarkNet backbone CNN.

arXiv admin note: substantial text overlap with arXiv:2305.01936

References in corpus (4)

• YOLOv4: Optimal Speed and Accuracy of Object Detection
• DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
• Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey
• Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans

Cited by in corpus (2)

• Self-supervised visual learning for analyzing firearms trafficking activities on the Web
• Illicit object detection in X-ray images using Vision Transformers