Smartphone Based Real Time Cyclist Safety System Using Lightweight YOLOv5n Object Detection

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 ([email protected]) 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.