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Article

RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation

by
Gui Yu
1,2,3,4,
Juming Dong
2,
Yihang Wang
2 and
Xinglin Zhou
1,3,4,*
1
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
2
School of Mechanical and Electrical Engineering, Huanggang Normal University, Huanggang 438000, China
3
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
4
School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(1), 53; https://doi.org/10.3390/s23010053
Submission received: 19 October 2022 / Revised: 7 December 2022 / Accepted: 17 December 2022 / Published: 21 December 2022
(This article belongs to the Collection Convolutional Neural Networks Applications in Sensing and Imaging: Architectures, Insight, Visualization, Transparency)
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Abstract

Automatic crack detection is always a challenging task due to the inherent complex backgrounds, uneven illumination, irregular patterns, and various types of noise interference. In this paper, we proposed a U-shaped encoder–decoder semantic segmentation network combining Unet and Resnet for pixel-level pavement crack image segmentation, which is called RUC-Net. We introduced the spatial-channel squeeze and excitation (scSE) attention module to improve the detection effect and used the focal loss function to deal with the class imbalance problem in the pavement crack segmentation task. We evaluated our methods using three public datasets, CFD, Crack500, and DeepCrack, and all achieved superior results to those of FCN, Unet, and SegNet. In addition, taking the CFD dataset as an example, we performed ablation studies and compared the differences of various scSE modules and their combinations in improving the performance of crack detection.
Keywords: pavement crack segmentation; convolutional neural network; U-net; scSE attention mechanism module pavement crack segmentation; convolutional neural network; U-net; scSE attention mechanism module

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MDPI and ACS Style

Yu, G.; Dong, J.; Wang, Y.; Zhou, X. RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation. Sensors 2023, 23, 53. https://doi.org/10.3390/s23010053

AMA Style

Yu G, Dong J, Wang Y, Zhou X. RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation. Sensors. 2023; 23(1):53. https://doi.org/10.3390/s23010053

Chicago/Turabian Style

Yu, Gui, Juming Dong, Yihang Wang, and Xinglin Zhou. 2023. "RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation" Sensors 23, no. 1: 53. https://doi.org/10.3390/s23010053

APA Style

Yu, G., Dong, J., Wang, Y., & Zhou, X. (2023). RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation. Sensors, 23(1), 53. https://doi.org/10.3390/s23010053

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