Automatic assessment of concrete cracks in low-light, overexposed, and blurred images restored using a generative AI approach

Abstract

Deep learning-based computer vision techniques have high efficiency in assessing concrete cracks from images, and the assessment can be automated using robots for higher efficiency. However, assessment accuracy is often compromised by low-quality images. This paper presents a Conditional Generative Adversarial Network (CGAN)-based approach to restore low-light, overexposed, and blurred images. The approach integrates attention mechanisms and residual learning and uses Wasserstein loss with gradient penalty. Crack assessment results show that the proposed approach outperforms state-of-the-art methods, regarding structural similarity (SSIM: 0.78 for deblurring, 0.95 for low-light enhancement, and 0.96 for overexposure correction) and peak signal-to-noise ratio (PSNR: 28.6 for deblurring, 31.4 for low-light enhancement, and 31.6 for overexposure correction). Restored images have been used to train a deep learning model for assessing concrete cracks. The Intersection over Union (IoU) and F1 score of crack segmentation are higher than 0.98 and 0.99, respectively, revealing high accuracy in crack assessment tasks.