Enhancing Real-Time Detection Transformer (RT-DETR) for Handgun Detection on Nvidia Jetson
Recent studies have highlighted the rise in violence and criminal activities primarily involving firearms. In response to the growing demand for effective firearm detec- tion systems in public safety applications, this paper explores advancements in real- time object detection using Transformer-bas...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Centro Latinoamericano de Estudios en Informática
2025-05-01
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| Series: | CLEI Electronic Journal |
| Online Access: | https://clei.org/cleiej/index.php/cleiej/article/view/802 |
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| Summary: | Recent studies have highlighted the rise in violence and criminal activities primarily
involving firearms. In response to the growing demand for effective firearm detec-
tion systems in public safety applications, this paper explores advancements in real-
time object detection using Transformer-based models. Building on the RT-DETR
architecture and its latest version, RT-DETR v2, we introduce improvements such as
the Bidirectional Feature Pyramid Network (BiFPN) for enhanced small object de-
tection and dynamic batch processing to maximize computational efficiency and re-
source utilization on edge devices like the Nvidia Jetson AGX Xavier for efficient real-
time deployment. We also compare the model with state-of-the-art alternatives such
as YOLOv10, demonstrating the superiority of Transformer models in terms of accu-
racy and performance. For the comparative study, we used a benchmark proposing
three datasets with challenging conditions. Code and trained models are available at
https://github.com/labt1/GunDetection-RTDETR.
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| ISSN: | 0717-5000 |