Real-Time Road Hazard Classification Using Object Detection with Deep Learning

Potholes and speed bumps are common road hazards that can cause vehicle damage and put drivers in danger. Potholes, or road imperfections, are hazardous to both vehicles and people. This research proposes an innovative deep learning framework relying on the YOLOv8 architecture. For bettering the model's accuracy and resilience, it is being improved on distinctive annotated dataset. The dataset includes images of road surfaces with annotated potholes and speed bumps to help the model recognize these features. The model uses the power of convolutional neural networks to analyze road images and make high-accuracy predictions. The proposed system can be integrated into vehicles and other transportation systems to provide drivers with timely and reliable alerts, improving road safety and reducing vehicle damage. The experiments show that the approach is effective at detecting potholes and speed bumps with good precision, recall, mAP, and F1-score, providing an innovative solution for real-time pothole and speed bump detection.