Article
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Dangerous Items Detection in Surveillance Camera Images Using Faster R-CNN
Version 1
: Received: 13 June 2024 / Approved: 16 June 2024 / Online: 17 June 2024 (10:25:59 CEST)
How to cite: Omiotek, Z.; Zhunissova, U. Dangerous Items Detection in Surveillance Camera Images Using Faster R-CNN. Preprints 2024, 2024061090. https://doi.org/10.20944/preprints202406.1090.v1 Omiotek, Z.; Zhunissova, U. Dangerous Items Detection in Surveillance Camera Images Using Faster R-CNN. Preprints 2024, 2024061090. https://doi.org/10.20944/preprints202406.1090.v1
Abstract
Recently, a significant number of tools for detecting dangerous objects have been developed. Unfortunately, the performance offered by them is overestimated due to the poor quality of the datasets used (insufficiently numerous, contain items not strictly related to dangerous objects, insufficient range of presentation conditions). To fill in a gap in this area we have built an extensive dataset dedicated to detecting the objects most often used in various acts of breaching public security (baseball bat, gun, knife, machete, rifle). This collection contains images presenting the detected objects with different quality and under different environmental conditions. We believe that the results obtained from it are more reliable and give a better idea of the detection accuracy that can be achieved under real conditions. We used the Faster R-CNN with different backbone networks in the study. The best results were obtained for the ResNet152 backbone. The mAP value was 85%, while the AP level ranged from 80% to 91%, depending on the item detected. An average real-time detection speed was 11-13 FPS. Both the accuracy and speed of the Faster R-CNN model allow it to be recommended for use in public security monitoring systems aimed at detecting potentially dangerous objects.
Keywords
object detection; faster R-CNN; deep learning; dangerous objects; public safety
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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