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

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.