Year: 2024
Type: article
Abstract: Gun violence is a global problem that affects communities and individuals, posing challenges to safety and well-being. The use of autonomous weapons detection systems could significantly improve security worldwide. Despite notable progress in the field of weapons detection closed-circuit television-based systems, several challenges persist, including real-time detection, improved accuracy in detecting small objects, and reducing false positives. This paper, based on our extensive experience in this field and successful private company contracts, presents a detection scheme comprising two modules that enhance the performance of a renowned detector. These modules not only augment the detector’s performance but also have a low negative impact on the inference time. Additionally, a scale-matching technique is utilised to enhance the detection of weapons with a small aspect ratio. The experimental results demonstrate that the scale-matching method enhances the detection of small objects, with an improvement of +13.23 in average precision compared to the non-use of this method. Furthermore, the proposed detection scheme effectively reduces the number of false positives (a 71% reduction in the total number of false positives) of the baseline model, while maintaining a low inference time (34 frames per second on an NVIDIA GeForce RTX-3060 card with a resolution of 720 pixels) in comparison to the baseline model (47 frames per second).
Source: Applied Sciences
DOI: 10.3390/app14188198
Institution: Universidad de Sevilla
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Funders: Nvidia, Agencia Estatal de Investigación
Awards: G2262748287, G3480869486, G425668148
OpenAlex ID: W4402494447