mlsanityData to 14 Oct 2026

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

arXiv 2310.03658 ↗First posted 5 Oct 2023cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 35 of its 52 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

35 of 52 references.

References · 35

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23 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Self-supervised visual learning for analyzing firearms trafficking activities on the Webcited by 3
  2. 2024Illicit object detection in X-ray images using Vision Transformerscited by 3
  3. 2025X-ray illicit object detection using hybrid CNN-transformer neural network architecturescited by 0