RT-DETR Edge Deployment: Real-Time Detection Transformer for Distracted Driving Detection

Distracted driving is one of the primary contributors to road accidents worldwide, highlighting the urgent need for reliable in-cabin driver monitoring systems. Existing approaches often face trade-offs: CNN-based classifiers achieve high recognition accuracy but lack spatial localization, while lightweight real-time detectors sacrifice contextual reasoning for efficiency. To bridge this gap, we propose a customized fine-tuned transformer-based object detection framework, RT-DETR-L, specifically adapted for distracted driving detection. In contrast to prior applications of RT-DETR, our adaptation integrates distraction-specific data augmentation, loss-balancing strategies, and deployment-oriented optimizations, enabling precise classification and spatial localization of distractions such as texting, drinking, yawning, and eye closure. Trained and validated on a large-scale annotated in-cabin dataset, RT-DETR-L achieves state-of-the-art performance with a mAP50 of 0.995 and mAP50–95 of 0.774. In addition the proposed model demonstrates the deployment feasibility on resource-constrained embedded platforms (ARM-based edge AI devices), where the model sustains real-time performance at 17.5 FPS with minimal latency. These results establish RT-DETR-L as a hybrid solution combining the semantic depth of transformers with the efficiency required for Advanced Driver Assistance Systems (ADAS). By addressing both accuracy and deployability, this study makes concrete contributions toward advancing robust, real-time driver monitoring for enhanced road safety.