Measurement for cracks at the bottom of bridges based on tethered creeping unmanned aerial vehicle

• Published in: Automation in construction 2020-11, Vol.119, p.103330, Article 103330
• Main Authors: Wang, Hui-Feng, Zhai, Lei, Huang, He, Guan, Li-Min, Mu, Ke-Nan, Wang, Gui-ping
• Format: Article
• Language: English
• Subjects: Algorithms; Bridge maintenance; Cracks; Error analysis; Evaluation; Feature matching; Flaw detection; Image detection; Image filters; Image restoration; Image stitching; Instrumentation technology; Maximum spanning tree; Multi-band blending; Stitching; Structural health monitoring; Tethered creeping UAV; Unmanned aerial vehicles
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2020.103330

The detection of bridge bottom cracks is required for bridge maintenance. In order to realise the requirement of automatic real-time detection of a bridge structure, an image detection method for cracks in the bottom of the bridge structure using a tethered creeping unmanned aerial vehicle (UAV) is proposed. A high-precision unlimited endurance detection plan based on the tethered creeping UAV is designed to use for the bottom cracks of the bridge structure. The detection scheme applies a high-precision image stitching measurement algorithm for cracks at the bottom of the beam body, which is able to restore a panoramic image. All mainstream filtering methods were evaluated, and it turned out that they are practicable/applicable in various crack images of different shapes. The method is applied to the crack detection in the bottom of bridge structures to ensure the accuracy and efficiency of the system measurement. According to the actual measurement by the laboratory platform, the measurement error using this method is less than 0.1 mm, which meets the requirements of measurement automation. The results of the research represent an initial step towards developing an automatic bridge health monitoring and evaluating system. •A feature point stitching algorithm suitable for crack images is proposed.•A crack segmentation algorithm combined with deep learning is proposed.•Designed a detection platform based on tethered UAV•The calculation accuracy of the crack parameters is less than 0.1 mm.