Real-Time Handgun Detection Using Transformers on Nvidia Jetson AGX Xavier

Bustamante L.A.; Gutierrez J.C. · Proceedings - 2024 50th Latin American Computing Conference, CLEI 2024 (2024) · DOI: 10.1109/clei64178.2024.10700426

Resumen

This study addresses the pressing need for enhanced security measures in Peru, where recent data from an INEI survey indicates that 25% of residents in major cities have fallen victim to crime. We present an innovative approach to bolstering public safety by employing Transformer neural networks for real-time firearm detection in surveillance cameras. Diverging from conventional methods such as YOLO, our research capitalizes on the capabilities of the Real-Time Detection Transformer (RT-DETR), which utilizes a Hybrid Encoder to facilitate real-time object detection without compromising accuracy. Our model, evaluated on Nvidia Jetson AGX Xavier, achieved a remarkable F1 score of 99.52 % at 38 frames per second, affirming the feasibility of deploying Transformer models on low-power embedded devices by implementing in CUDA. Our findings indicate that Transformer models have the potential to significantly enhance real-time threat detection and fortify urban security infrastructure, presenting a proactive solution to combat the rising challenge of firearm-related crimes. Source code available at https://github.com/labt1/GunDetection-RTDETR.

Instituciones: Universidad Nacional de San Agustín de Arequipa