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Visual inspection for illicit items in X-ray images using Deep Learning
Computer Vision and Pattern Recognition · Submitted 5 Oct 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.05 | 0.02 | hedged possibility 0.65 |
| s1 | The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem. | background 1.00 | 0.07 | 0.01 | asserted 0.97 |
| s2 | 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. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s3 | 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. | gap 1.00 | 0.08 | 0.02 | asserted 1.00 |
| s4 | 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. | contribution 0.99 | 0.91 | 0.04 | asserted 1.00 |
| s5 | 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. | result 0.96 | 0.94 | 0.94 | hedged possibility 0.54 |
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The paper
Original 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.
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1 arXiv paper cite this in 1 sentence
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Cites as background 2 papers
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YOLOv5 [ref] , which is an update of YOLOv4 [ref] , is inspired by EfficientNet [ref] and, thus, can be easily reconfigured for different network complexity profiles.
YOLOv4: Optimal Speed and Accuracy of Object Detection · Detection Heads · 0.91
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One of the most important relevant architectures is MobileNet, which accelerates inference by incorporating “separable convolutions" [ref] .
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications · Backbone Networks · 0.90
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