IMAGE PROCESSING AND DEEP LEARNING FOR EXCELLENT ASPHALT CRACK DETECTION

This study addresses the critical task of asphalt crack detection, essential for efficient road maintenance and infrastructure management. Traditional methods using raw road surface images often suffer from low detection accuracy under varied conditions. To overcome these limitations, our approach integrates advanced image-processing techniques that enhance input image quality prior to training, thus improving model generalization across diverse road conditions. This method significantly boosts detection performance, offering a reliable solution for civil engineering applications. The initial accuracy rates were 74.54% for Unet and 91.45% for CNN, which improved post-processing to 97.58% and 100%, respectively.