Crack segmentation-guided measurement with lightweight distillation network on edge device
Zhang, Jianqi; Ding, Ling; Wang, Wei; Wang, Hainian; Brilakis, Ioannis; Davletshina, Diana; Heikkila, Rauno; Yang, Xu (2025-03-03)
John Wiley & Sons
03.03.2025
Zhang, J., Ding, L., Wang, W., Wang, H., Brilakis, I., Davletshina, D., Heikkilä, R., & Yang, X. (2025). Crack segmentation-guided measurement with lightweight distillation network on edge device. Computer-Aided Civil and Infrastructure Engineering, 40, 2269–2286. https://doi.org/10.1111/mice.13446
https://creativecommons.org/licenses/by/4.0/
© 2025 The Author(s). Computer-Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
https://creativecommons.org/licenses/by/4.0/
© 2025 The Author(s). Computer-Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202504152674
https://urn.fi/URN:NBN:fi:oulu-202504152674
Tiivistelmä
Abstract
Pavement crack measurement (PCM) is essential for automated, precise road condition assessment. However, balancing speed and accuracy on edge artificial intelligence (AI) mobile devices remains challenging. This paper proposes a real-time PCM framework for edge deployment, incorporating a lightweight distillation network and a surface feature measurement algorithm. Specifically, the proposed instance-aware hybrid distillation module combines feature-based and relation-based knowledge distillation, leveraging crack instance-related information for efficient knowledge transfer from teacher to student networks, which results in a more accurate and lightweight segmentation model. Additionally, a real-time crack surface feature measurement algorithm, based on distance mapping relationships and crack edge coordinate extraction, addresses issues with crack edge branching and loss, enhancing measurement efficiency. Real-time measurement was performed on actual roads utilizing mobile robot equipped with an edge computing unit. The crack segmentation precision reached 84.37%, with a frame per second of 77.72. Compared to the ground truth, the relative error for average crack width ranged from 6.42% to 40.65%, while the relative error for crack length varied between 1.48% and 3.76%. These findings highlight the feasibility of real-time crack assessment and save road maintenance costs.
Pavement crack measurement (PCM) is essential for automated, precise road condition assessment. However, balancing speed and accuracy on edge artificial intelligence (AI) mobile devices remains challenging. This paper proposes a real-time PCM framework for edge deployment, incorporating a lightweight distillation network and a surface feature measurement algorithm. Specifically, the proposed instance-aware hybrid distillation module combines feature-based and relation-based knowledge distillation, leveraging crack instance-related information for efficient knowledge transfer from teacher to student networks, which results in a more accurate and lightweight segmentation model. Additionally, a real-time crack surface feature measurement algorithm, based on distance mapping relationships and crack edge coordinate extraction, addresses issues with crack edge branching and loss, enhancing measurement efficiency. Real-time measurement was performed on actual roads utilizing mobile robot equipped with an edge computing unit. The crack segmentation precision reached 84.37%, with a frame per second of 77.72. Compared to the ground truth, the relative error for average crack width ranged from 6.42% to 40.65%, while the relative error for crack length varied between 1.48% and 3.76%. These findings highlight the feasibility of real-time crack assessment and save road maintenance costs.
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