• Gautam Rulyan

Cyclist fatalities are increasing globally—1,155 deaths were recorded in the US alone in 2023. Many of these incidents involve single-bicycle crashes caused by road hazards such as potholes, debris, or animals. This paper presents a smartphone-based real-time hazard detection system designed to enhance cyclist safety by providing timely warnings using a lightweight AI model. The system leverages YOLOv5n, an ultra-efficient object detection model optimized with TensorFlow Lite and OpenCV. YOLOv5n was selected over other lightweight models like MobileNet SSD or YOLOv6-lite for three reasons: (1) it offers a superior balance of inference speed and detection accuracy on mid-range smartphones; (2) its modular PyTorch implementation and quantization pipelines are deployment-friendly; and (3) it consistently outperformed MobileNet SSD in detecting small hazards while using less memory than YOLOv6-lite. The system detects both static (e.g., potholes, debris) and dynamic (e.g., vehicles, pedestrians) hazards from live video streams and issues alerts. Trained on a curated dataset of 2,000+ images, it achieved a mean Average Precision (mAP@0.5) of 0.86 and operated at 12.8–13.4 FPS on mid-range smartphones. Field tests showed an alert lead time of 1.2 ± 0.2 seconds, enhancing rider response and braking time. This can reduce impact risk by over 35% in urban cycling conditions [1]. Unlike server-based or embedded alternatives, this privacy-preserving solution runs entirely on-device. Aligning with Vision Zero and sustainable mobility goals [2], it can integrate into municipal hazard systems or cyclist insurance incentives, fostering both safety and behavioral accountability.

Cyclist Safety, YOLOv5n, Real-Time Object Detection, Smartphone-Based AI, Road Hazard Detection, TensorFlow Lite, Mobile Vision Systems, Edge Computing, Urban Mobility, Vision Zero.

"Smartphone-Based Real-Time Cyclist Safety System Using Lightweight YOLOv5n Object Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.e643-e648, July-2025, Available :http://www.jetir.org/papers/JETIR2507491.pdf

"Smartphone-Based Real-Time Cyclist Safety System Using Lightweight YOLOv5n Object Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppe643-e648, July-2025, Available at : http://www.jetir.org/papers/JETIR2507491.pdf