Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and Camera
Cracks are the main damages of concrete structures. Since cracks may occur in areas that are difficult to reach, non-contact measurement technology is required to accurately measure the width of cracks. This study presents an innovative computer vision system combining a camera and laser rangefinder...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10651 |
| _version_ | 1827651841504051200 |
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| author | Sizeng Zhao Fei Kang Junjie Li |
| author_facet | Sizeng Zhao Fei Kang Junjie Li |
| author_sort | Sizeng Zhao |
| collection | DOAJ |
| description | Cracks are the main damages of concrete structures. Since cracks may occur in areas that are difficult to reach, non-contact measurement technology is required to accurately measure the width of cracks. This study presents an innovative computer vision system combining a camera and laser rangefinder to measure crack width from any angle and at a long distance. To solve the problem of pixel distortion caused by non-vertical photographing, geometric transformation formulas that can calculate the unit pixel length of the image captured at any angle are proposed. The complexity of crack edge calculation and the imbalance of data in the image are other problems that affect measurement accuracy, and a combination of the improved U-net convolutional networks algorithm and Canny edge detection method is adopted to accurately extract the cracks. The measurement results on the different concrete wall indicate that the proposed system can measure the crack in a non-vertical position, and the proposed algorithm can extract the crack from different background images. Although the proposed system cannot achieve fully automated measurement, the results also confirm the ability to obtain the crack width accurately and conveniently. |
| first_indexed | 2024-03-09T20:45:13Z |
| format | Article |
| id | doaj.art-ecb5c987917e4416b2f3c61b2f652b77 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:45:13Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ecb5c987917e4416b2f3c61b2f652b772023-11-23T22:48:13ZengMDPI AGApplied Sciences2076-34172022-10-0112201065110.3390/app122010651Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and CameraSizeng Zhao0Fei Kang1Junjie Li2School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, ChinaCracks are the main damages of concrete structures. Since cracks may occur in areas that are difficult to reach, non-contact measurement technology is required to accurately measure the width of cracks. This study presents an innovative computer vision system combining a camera and laser rangefinder to measure crack width from any angle and at a long distance. To solve the problem of pixel distortion caused by non-vertical photographing, geometric transformation formulas that can calculate the unit pixel length of the image captured at any angle are proposed. The complexity of crack edge calculation and the imbalance of data in the image are other problems that affect measurement accuracy, and a combination of the improved U-net convolutional networks algorithm and Canny edge detection method is adopted to accurately extract the cracks. The measurement results on the different concrete wall indicate that the proposed system can measure the crack in a non-vertical position, and the proposed algorithm can extract the crack from different background images. Although the proposed system cannot achieve fully automated measurement, the results also confirm the ability to obtain the crack width accurately and conveniently.https://www.mdpi.com/2076-3417/12/20/10651computer visioncrack measurementU-netconvolutional neural networkstructural health monitoringartificial intelligence |
| spellingShingle | Sizeng Zhao Fei Kang Junjie Li Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and Camera Applied Sciences computer vision crack measurement U-net convolutional neural network structural health monitoring artificial intelligence |
| title | Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and Camera |
| title_full | Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and Camera |
| title_fullStr | Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and Camera |
| title_full_unstemmed | Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and Camera |
| title_short | Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and Camera |
| title_sort | non contact crack visual measurement system combining improved u net algorithm and canny edge detection method with laser rangefinder and camera |
| topic | computer vision crack measurement U-net convolutional neural network structural health monitoring artificial intelligence |
| url | https://www.mdpi.com/2076-3417/12/20/10651 |
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