Advancing Road Infrastructure: An Integrated Approach for Accurate Pothole Detection and Mapping in Developing Countries

This study introduces an innovative system to enhance road safety and maintenance in developing countries through precise pothole detection and mapping. Leveraging deep learning, YOLOv4, and diverse backbone networks like MobileNetv2, ResNet18, ResNetlOl, VGG16, and VGG19, the system achieves superior accuracy. Here, transfer learning is used with a combination of online and Fiji pothole datasets. Evaluation highlights YOLOv4's ResNetlOl outperformance with an average precision of 0.903, mean average precision of 0.546 and F1 – score of 0.905. For the pothole detection system, MATLAB is used to create a real time data acquisition application with post processing capabilities. Using this system, a dataset of image and location data points were recorded and processed. The feasibility of this system highlighted the innovative integration of deep learning and sensors for fast and efficient pothole detection and mapping. As road safety pioneers, our research not only advances infrastructure but also elevates road networks' resilience and sustainability.