Pavement crack detection method based on multi-scale feature enhancement

ZHAI Jun-zhi; SUN Zhao-yun; PEI Li-li; HUYAN Ju; LI Wei

doi:10.19818/j.cnki.1671-1637.2023.01.022

Volume 23 Issue 1

Feb. 2023

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Article Navigation > Journal of Traffic and Transportation Engineering > 2023 > 23(1): 291-308

ZHAI Jun-zhi, SUN Zhao-yun, PEI Li-li, HUYAN Ju, LI Wei. Pavement crack detection method based on multi-scale feature enhancement[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 291-308. doi: 10.19818/j.cnki.1671-1637.2023.01.022

Citation:

ZHAI Jun-zhi, SUN Zhao-yun, PEI Li-li, HUYAN Ju, LI Wei. Pavement crack detection method based on multi-scale feature enhancement[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 291-308. doi: 10.19818/j.cnki.1671-1637.2023.01.022

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Pavement crack detection method based on multi-scale feature enhancement

doi: 10.19818/j.cnki.1671-1637.2023.01.022

1.
School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
2.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China

Funds:

National Key Research and Development Program of China 2021YFB1600205

National Natural Science Foundation of China 52178407

National Natural Science Foundation of China 51978071

Key Research and Development Program of Shaanxi Province 2022JBGS3-08

Fundamental Research Funds for the Central Universities 300102242901

More Information

Author Bio:
ZHAI Jun-zhi(1981-), male, doctoral student, zjz5250@sina.cn

SUN Zhao-yun(1962-), female, professor, PhD, zhaoyunsun@126.com
Received Date: 2022-09-02
Publish Date: 2023-02-25

Abstract

Abstract

To solve the problems of incomplete pavement crack detection and discontinuous segmentation, a detection network MFENet for pavement cracks based on multi-scale feature enhancement was proposed, and the detection, classification and segmentation of end-to-end pavement crack images were realized. A multi-scale attention-based feature enhancement module was designed, and the mapping relationships of the weight coefficients of the upper multi-scale feature channels with those of the lower feature channels in the network model were determined to highlight the feature outputs from the effective channels. Based on the correlation between the coordinate information of the pavement crack and the semantic information of the pixels in physical location, a multi-semantic feature correlation module was designed and thereby feature fusion and enhancement among different semantic information were achieved. Then, the foreground features of the pavement crack image were filtered by feature dimension transformation. A quantitative evaluation method for deep feature intensity was proposed to improve the interpretability of the model's feature extraction ability. Research results on self-collected dataset show that the average precision and average recall of the MFENet in pavement crack image detection are 4.3% and 5.4% higher than those of the Mask R-CNN, respectively, and 14.6% and 14.3% higher than those of the baseline model RDSNet, respectively. The average precision and average recall of the MFENet in pavement crack image segmentation are 6.6% and 8.8% higher than those of the Mask R-CNN, respectively, and 8.1% and 9.7% higher than those of the RDSNet, respectively. In the comparison with the Mask R-CNN and other mainstream methods, the images of different types of pavement cracks are detected and segmented with the highest accuracy by the MFENet. Research results on public datasets (CFD and CRACK500) show that the detection and segmentation accuracy of the MFENet are invariably higher than those of the Mask R-CNN and other mainstream methods on the datasets covering different scenarios, indicating the higher robustness of the proposed method. In addition, the processing speed of the MFENet is also faster than that of the RDSNet on different datasets.
- pavement crack detection,
- multi-scale attention,
- feature enhancement,
- multi-semantic,
- interpretability,
- robustness

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References(32)

References

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