Pothole Detection using Deep Learning (YOLO v2) and Image Processing

This study presents a robust approach for automated pothole detection leveraging Deep Learning techniques, specifically utilizing YOLO v2 (You Only Look Once) object detection framework and comprehensive image processing methodologies. The process begins with the acquisition of a diverse dataset sourced from Google, annotated meticulously using the Ground Truth Labeller app to establish accurate ground truth labels for each detector. Subsequently, Convolutional Neural Network (CNN) layers are meticulously designed, tailored to enhance feature extraction and classification capabilities. Training options are carefully configured to optimize model performance and efficiency. The implementation of the YOLO v2 object detector integrates seamlessly with the designed data store, layers, and configuration options, enabling real-time detection and localization of potholes within input images. The system outputs include detailed counts and spatial visualizations of detected potholes, facilitating prompt maintenance and infrastructure management decisions. This research contributes to advancing automated inspection systems, enhancing road safety, and optimizing maintenance operations through efficient detection and monitoring of roadway defects.

Keywords: Pothole Images Dataset, Pre-Processing, YOLOv2 Detection, Ground truth Labeler app, Deep learning.