Abstract
Traditional road inspections are manual processes, prone to human error and inefficiencies. This paper presents a novel approach for automated roadway inspection using a Convolutional Neural Network (CNN) model. Our system leverages computer vision techniques to detect potholes and speed breakers on road surfaces from images. We developed a CNN model trained on a comprehensive dataset of road images containing various pothole and speed breaker types, lighting conditions, and road backgrounds. The model achieved an accuracy of 93% in detecting these road defects, demonstrating the effectiveness of deep learning for automated roadway inspections. This system has the potential to significantly improve the efficiency and objectivity of road inspections, leading to faster repairs and improved road safety
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