| Summary: | Current deblurring methods struggle with real-world scenarios where images are often blurred or noisy, posing significant challenges to existing pavement crack detection techniques. Thus, the aim of this research is to develop and evaluate a novel approach utilizing a nonlinear activation-free network (NAFNet) to address image blurring as a preprocessing step, with the primary goal of improving the reliability and accuracy of pavement crack detection in standard datasets and real-world pavement images under various challenging conditions. The scope of this study is to enhance pavement crack detection by developing a robust and accurate NAFNet designed specifically for road image deblurring, evaluated using standard pavement crack datasets. We adopt NAFNet, which innovatively replaces batch normalization with pixel-level layer normalization and utilizes a U-Net structure with skip connections and optimized the network with SGD (NAFNet-SGD). From the experimental results, quantitatively, the NAFNet-SGD model outperformed the others, achieving the highest PSNR of 32.8642 and an SSIM of 0.9605, while qualitatively, images processed with NAFNetSGD exhibited the highest quality with superior visual clarity and sharpness. Thus, in conclusion, NAFNet-SGD outperforms other optimizers like Adam and AdamW in terms of both quantitative metrics and visual quality. © 2024 IEEE.
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