Automatic Visual Traffic Sign Damage Detection and Measurement of Damaged Area

This paper leverages advanced computer vision and machine learning techniques to accurately quantify the level of damage sustained by traffic signs. Utilizing a combination of novel technologies, this study aims to provide a comprehensive solution for assessing the severity of damage and expediting necessary maintenance. The methodology incorporates the YOLO (You Only Look Once) algorithm for traffic sign detection, and an autoencoder to generate reconstructed images of the detected signs. By comparing the quality of these generated images with the detected one, the extent of damage is quantitatively evaluated using the Structural Similarity Index (SSIM). This integrated approach combines detection and generation techniques, offering a sophisticated framework for efficiently measuring the level of damage sustained by traffic signs, thereby enhancing road safety and optimizing traffic flow. The experimental results demonstrate the effectiveness of the proposed method in accurately detecting and assessing traffic sign damage, paving the way for more reliable and efficient traffic sign maintenance strategies.