Content-Aware Explainable Pavement Distress Detection Model

Li Ao; Ge Yongxin; Liu Huijun; Yang Chunhua; Zhou Xiuzhuang

doi:10.7544/issn1000-1239.202220795

Journal of Computer Research and Development > 2024 > 61(3): 701-715. > DOI: 10.7544/issn1000-1239.202220795 CSTR: 32373.14.issn1000-1239.202220795

Li Ao, Ge Yongxin, Liu Huijun, Yang Chunhua, Zhou Xiuzhuang. Content-Aware Explainable Pavement Distress Detection Model[J]. Journal of Computer Research and Development, 2024, 61(3): 701-715. DOI: 10.7544/issn1000-1239.202220795

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Content-Aware Explainable Pavement Distress Detection Model

1.
School of Big Data & Software Engineering, Chongqing University, Chongqing 401331
2.
School of Intelligent Engineering, Chongqing City Management College, Chongqing 401331
3.
College of Computer Science, Chongqing University, Chongqing 400044
4.
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876

Funds: This work was supported by the National Natural Science Foundation of China (62176031, 61972046)，the Natural Science Foundation of Chongqing (CSTB2022NSCQ-MSX1405)，and the Specialized Project for Technology Innovation and Application Development of Chongqing (CSTB2022TIAD-KPX0100).

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Author Bio:
Li Ao: born in 1994. Master. His main research interests include computer vision and deep learning

Ge Yongxin: born in 1981. PhD, professor, PhD supervisor. His main research interests include computer vision, machine learning, and big data intelligence

Liu Huijun: born in 1975. PhD, associate professor, master supervisor. His main research interests include data mining, big data intelligence, and computer vision

Yang Chunhua: born in 1998. Master. His main research interests include deep learning and digital image processing

Zhou Xiuzhuang: born in 1974. PhD, professor, PhD supervisor. His main research interests include computer vision, machine learning, and medical AI
Received Date: September 05, 2022
Revised Date: May 14, 2023
Available Online: November 29, 2023

Graphical Abstract

Abstract

Abstract

To address the challenges of using high-resolution pavement images as input for existing convolutional neural network models and the inability of existing preprocessing algorithms to effectively perceive and retain information from low-ratio distress regions in original pavement images, a novel architectural unit called adaptive perception module (APM) paying greater attention to pavement distress region is proposed with the help of visual interpretation techniques, which achieves a rapid and accurate detection of pavement distress in high-resolution images and could be used to build a software system for automatic detection of pavement distress based on computer vision. Firstly, big kernel convolution and residual operations are used to reduce the origin image resolution and get the low-level but rich feature representation. Secondly, attention mechanism is developed to perceive and activate the region of pavement distress and filter the irrelevant background pixel noise. By means of joint learning, APM training could be completed without additional cost. After the visual interpretation method is used to aid the selection and design of the specific structure of APM, experimental results on the latest public dataset CQU-BPMDD show that the proposed APM significantly improves the classification accuracy, up to 84.47%. Experiments across different datasets CQU-BPDD demonstrate the generalization and robustness of APM. Code is available on https://github.com/Li-Ao-Git/apm.
- pavement distress detection,
- explainability,
- adaptive perception,
- attention mechanism,
- joint learning

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References (42)

References

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