Motorcycle safety is significantly compromised by blind spot collisions, necessitating Advanced Rider Assistance Systems. This paper proposes a blind spot warning system designed for edge devices, leveraging computer vision techniques for real-time object detection and tracking. The system aims to enhance rider awareness by detecting vehicles in blind spots and providing timely warnings through visual LED alerts. Preliminary results from training the RF-DETRBase model, the lightest version of the RF-DETR architecture, on the challenging BDD100K dataset, which features diverse driving scenarios and numerous small objects, demonstrate the system’s potential. The dataset’s inherent complexities were highlighted by poor small object detection (mAP 0.128) and declining performance at higher IoU thresholds. Despite these challenges, the model achieved a promising Average Precision of 0.700 at an Intersection over Union of 0.50, indicating effective vehicle detection.