Data Augmentation by an Additional Self-Supervised CycleGAN-Based for Shadowed Pavement Detection

Jiajun Song, Peigen Li, Qiang Fang, Haiting Xia () and Rongxin Guo
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Jiajun Song: Yunnan Key Laboratory of Disaster Reduction in Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China
Peigen Li: Yunnan Key Laboratory of Disaster Reduction in Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China
Qiang Fang: Yunnan Key Laboratory of Disaster Reduction in Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China
Haiting Xia: Yunnan Key Laboratory of Disaster Reduction in Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China
Rongxin Guo: Yunnan Key Laboratory of Disaster Reduction in Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China

Sustainability, 2022, vol. 14, issue 21, 1-16

Abstract: With the rapid development of deep learning, pavement crack detection has started to shift from traditional manual visual inspection to automated detection; however, automatic detection is still a challenge due to many complex interference conditions on pavements. To solve the problem of shadow interference in pavement crack detection, this paper proposes an improved shadow generation network, named Texture Self-Supervised CycleGAN (CycleGAN-TSS), which can improve the effect of generation and can be used to augment the band of shadowed images of pavement cracks. We selected various images from three public datasets, namely Crack500, cracktree200, and CFD, to create shadowed pavement-crack images and fed them into CycleGAN-TSS for training to inspect the generation effect of the network. To verify the effect of the proposed method on crack segmentation with shadow interference, the segmentation results of the augmented dataset were compared with those of the original dataset, using the U-Net. The results show that the segmentation network achieved a higher crack recognition accuracy after the augmented dataset was used for training. Our method, which involves generating shadowed images to augment the dataset and putting them into the training network, can effectively improve the anti-shadow interference ability of the crack segmentation network. The research in this paper also provides a feasible method for improving detection accuracy under other interference conditions in future pavement recognition work.

Keywords: CycleGANs; shadow interference; data augmentation; crack detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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