Abstract
To address the problem of decreasing disease detection accuracy due to shadow interference in complex road scenes, this study proposed a road shadow removal algorithm based on a weakly supervised generative adversarial network. By constructing a dynamic adaptive normalization strategy and an improved residual module, the algorithm optimized image feature representation under the condition of limited shadow pairing samples. The specific design was as follows:First, an auxiliary classifier with an attention mechanism was introduced to enhance the semantic perception ability of the network for road scenes. Second, the residual structure was improved by adopting cross-layer skip connections to effectively alleviate the problem of gradient vanishing in deep layers of the network. Finally, a hybrid loss function that combined the perceptual loss and the adversarial loss was designed to enhance the structure preservation ability of shadow removal. The experimental results show that the image quality evaluation metrics ERMSE,RPSNR, and SSSIM of the shadow removal algorithm are 1.083 4,27.568 2, and 0.794 9. The images after shadow removal using the proposed algorithm were fed into the detection model. The recognition accuracy of six typical road diseases such as cracks and potholes are improved by 3.3% ‒10.2%, which verifies the effectiveness of the method.
Publication Date
8-16-2026
DOI
10.14048/j.issn.1671-2579.2026.04.008
First Page
72
Last Page
81
Submission Date
August 2026
Recommended Citation
Shengbin, Deng; Ming, Li; Lijia, Ye; and Jing, Hu
(2026)
"Pavement shadow removal and disease detection based on weakly supervised generative adversarial network,"
Journal of China & Foreign Highway: Vol. 46:
Iss.
4, Article 8.
DOI: 10.14048/j.issn.1671-2579.2026.04.008
Available at:
https://zwgl1980.csust.edu.cn/journal/vol46/iss4/8
Reference
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