CFC-GAN: Forecasting Road Surface Crack Using Forecasted Crack Generative Adversarial Network

Forecasting the road surface crack images with given present crack images is an important task to assist the road survivors in planning for their next lay down of road with the required financial assistance. We develop a Crack ForeCast (CFC) dataset comprised of road crack images captured with certain time intervals (months), the number of vehicles traveled (collected from toll plaza), and climatic information like temperature and precipitation. We have proposed a Conditional Forecasted Crack-Generative Adversarial Network (CFC-GAN) model to forecast the road surface crack images with various conditional factors. The proposed CFC-GAN model is trained in a paired end-to-end manner to avoid accumulative blurriness and to generate the appropriate forecasted crack images. Moreover, this paper introduces a new estimation loss function to improve the accuracy of the forecasted crack images. Wide experimental results were demonstrated to showcase the better performance of our model. The quantitative and qualitative analyses were made with various evaluation metrics like structural similarity, peak signal to noise ratio, inception score, Frechet inception distance, and intersection over union for the CFC-GAN model with the proposed dataset and other existing benchmark datasets.