Download pdf
A study on drone-based detection and recognition of concrete surface cracks in tunnels using advanced imaging and machine learning techniques
- 1 Southwest Jiaotong University, Sichuan, China
* Author to whom correspondence should be addressed.
Abstract
The purpose of this thesis is to use drones and machine learning algorithms for automating crack detection in tunnel systems. With the high resolution RGB cameras and LiDAR sensor in drones, you get the imagery and structural data required to inspect tunnels. The images are then fed through CNNs together with SVMs for detecting and classification cracks in concrete and other surfaces. With this automated mechanism, the process will no longer need manual effort, and the inspection will be more precise and safer. The study shows the efficiency of this hybrid approach, which has 92% detection rate, much better than traditional inspection. And it is also very good at reducing false positives, and produces more trustworthy results. Crack severity is sorted into hairline, medium and deep cracks to make the process of maintenance and repairs easier. According to the results, paired with drones and machine learning, tunnel inspections become more effective, and data collection and analysis greatly enhanced. This method has potential use cases in infrastructure monitoring and could possibly be used for other structural damage detection tasks in high-dimensional domains.
Keywords
drone technology, machine learning, crack detection, tunnel inspection, infrastructure monitoring
[1]. Ahmad, A. A. (2023). Application of machine learning in drone technology for tracking cattle movement. Indian Journal of Animal Research, 57(12), 1717-1724.
[2]. Boopathi, S. (2024). Advancements in machine learning and AI for intelligent systems in drone applications for smart city developments. In Futuristic e-Governance Security With Deep Learning Applications (pp. 15-45). IGI Global.
[3]. Brighente, A., et al. (2023). ADASS: Anti-Drone Audio Surveillance Sentinel via Embedded Machine Learning. In 2023 IEEE Sensors Applications Symposium (SAS). IEEE.
[4]. Al Shafian, S., Hu, D., & Yu, W. (2024). Deep learning enhanced crack detection for tunnel inspection. In International Conference on Transportation and Development 2024.
[5]. Jeong, S., et al. (2023). Crack inspection in tunnel structures by fusing information from a 3D light detection and ranging and pan-tilt-zoom camera system. Structures, 58. Elsevier.
[6]. O'Brien, D., et al. (2023). Automated crack classification for the CERN underground tunnel infrastructure using deep learning. Tunnelling and Underground Space Technology, 131, 104668.
[7]. Sjölander, A., et al. (2023). Towards automated inspections of tunnels: A review of optical inspections and autonomous assessment of concrete tunnel linings. Sensors, 23(6), 3189.
[8]. Machado, L. B., & Futai, M. M. (2024). Tunnel performance prediction through degradation inspection and digital twin construction. Tunnelling and Underground Space Technology, 144, 105544.
[9]. Aela, P., et al. (2024). UAV-based studies in railway infrastructure monitoring. Automation in Construction, 167, 105714.
[10]. Ouyang, A., et al. (2023). Automated pixel-level crack monitoring system for large-scale underground infrastructure–A case study at CERN. Tunnelling and Underground Space Technology, 140, 105310.
Cite this article
Lai,M. (2024). A study on drone-based detection and recognition of concrete surface cracks in tunnels using advanced imaging and machine learning techniques. Advances in Operation Research and Production Management,3,32-36.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal：Advances in Operation Research and Production Management
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).