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Pixel-level Road Crack Detection and Segmentation Based on Deep Learning

 Pixel-level Road Crack Detection and Segmentation Based on Deep Learning
Author(s): , ,
Presented at IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022, published in , pp. 1346-1352
DOI: 10.2749/nanjing.2022.1346
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This paper proposed an integrated framework for detecting and segmenting road cracks in complex backgrounds. Based on the latest real-time object detection algorithm, YOLOv5l6, a modified U-Net emb...
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Bibliographic Details

Author(s): (Hunan University, Changsha, Hunan, China; Hunan Key Laboratory of Damage Diagnosis of Engineering Structures, Changsha, Hunan, China)
(Hunan University, Changsha, Hunan, China; Hunan Key Laboratory of Damage Diagnosis of Engineering Structures, Changsha, Hunan, China)
(Hunan University, Changsha, Hunan, China; Hunan Key Laboratory of Damage Diagnosis of Engineering Structures, Changsha, Hunan, China)
Medium: conference paper
Language(s): English
Conference: IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022
Published in:
Page(s): 1346-1352 Total no. of pages: 7
Page(s): 1346-1352
Total no. of pages: 7
DOI: 10.2749/nanjing.2022.1346
Abstract:

This paper proposed an integrated framework for detecting and segmenting road cracks in complex backgrounds. Based on the latest real-time object detection algorithm, YOLOv5l6, a modified U-Net embedded Bottleneck and Attention mechanism modules was developed to segment crack pixels from the detected crack regions. Validation of the proposed approach was conducted based on a total of 150 images, which were taken from different backgrounds, angles, and distances. Based on the computation, the results derived from the YOLOv5l6-based crack detection had a mean average precision of 92%, and the mean intersection of the union of the modified U-Net was 87%, which is at least 11% higher than the original U-Net model. The results showed the integrated approach could be a potential basis for an automated road-condition evaluation scheme for road operation and maintenance.

Keywords:
deep learning object detection road engineering crack segmentation
Copyright: © 2022 International Association for Bridge and Structural Engineering (IABSE)
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