Road Damage Detection: A YOLObased Approach with Real-Time Mobile Deployment

Abstract

Timely detection of road surface damage plays a crucial role in maintaining transportation safety and reducing infrastructure repair costs. Traditional road inspection methods are time-consuming, labor-intensive, and often lack precision, particularly in regions with limited resources. This research proposes a lightweight, real-time road damage detection system using deep learningbased object detection models integrated into a mobile application. The study evaluates and compares three recent YOLO models YOLOv9s, YOLOv10s, and YOLOv12s trained on a custom-annotated dataset of road surface images. Each model is assessed based on detection accuracy (mAP50 and mAP50-95), computational complexity (GFLOPs), and inference speed. Among the three, YOLOv9s demonstrated the best overall performance, achieving 88.2% mAP50 and 52.8% mAP50-95 with an inference speed of 9.8 ms and 26.7 GFLOPs. In contrast, YOLOv10s and YOLOv12s achieved lower accuracy scores but provided faster inference speeds of 6.9 ms and 4.3 ms, respectively, with significantly reduced computational loads. Based on the evaluation, YOLOv9s was selected as the optimal model and exported in TensorFlow Lite format (.tflite) for integration into a Flutter-based Android mobile application. The final system enables users to detect road damage in real-time directly from their smartphones, with results displayed instantly without the need for cloud processing. The proposed solution bridges the gap between academic model development and practical deployment, offering a scalable, cost-effective tool for road maintenance in both urban and rural environments. By ensuring low latency, high detection accuracy, and lightweight design, this study contributes a robust framework for intelligent infrastructure monitoring and supports future smart city applications.