Deep Learning for Detecting Dangerous Objects in X-rays of Luggage †
Abstract
:1. Introduction
2. Description of the Baggage and Hand Luggage Images Set
3. Methods for Detecting Dangerous Objects in Images
4. Results and Discussion
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Class | Train, N = 1200 Samples | Test, N = 300 Samples |
---|---|---|
Shocker | 122 | 34 |
Ammunition | 109 | 28 |
Grenades | 135 | 31 |
Firearms | 180 | 46 |
Steel arms | 166 | 37 |
Model | mAR |
---|---|
YOLOv5 | 0.843 |
SSD | 0.672 |
DETR | 0.697 |
Ours | 0.871 |
Class | Average Recall |
---|---|
Shocker | 0.912 |
Ammunition | 0.714 |
Grenades | 0.839 |
Firearms | 1 |
Steel arms | 0.892 |
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Andriyanov, N. Deep Learning for Detecting Dangerous Objects in X-rays of Luggage. Eng. Proc. 2023, 33, 20. https://doi.org/10.3390/engproc2023033020
Andriyanov N. Deep Learning for Detecting Dangerous Objects in X-rays of Luggage. Engineering Proceedings. 2023; 33(1):20. https://doi.org/10.3390/engproc2023033020
Chicago/Turabian StyleAndriyanov, Nikita. 2023. "Deep Learning for Detecting Dangerous Objects in X-rays of Luggage" Engineering Proceedings 33, no. 1: 20. https://doi.org/10.3390/engproc2023033020