Abstract
Because the steel structure trestle has been in service under heavy load for a long time, the steel structure trestle is prone to cracks around the welds or bolt holes, which can lead to structural collapse in severe cases. Aiming at the characteristics of stable and high-quality images obtained by the unmanned consumer-grade camera monitoring system, this paper proposed structure health monitoring (SHM) system which is based on consumer-grade camera. The SHM system can identify crack damage and locate steadily in long term, which provides the technical support of practical application in intelligent SHM system. The method first performed edge detection on the trestle structure, followed by pixel-level semantic segmentation and crack localization. Canny edge detection algorithm was used to identify trestle structures in the camera image. The panorama trestle structure was divided into areas of suitable size, and the camera focused on each divided area one by one. Then the improved DeepLab V3+ model was trained by constructing global and local datasets. Then the improved DeepLab V3+ model was used to perform pixel-level semantic segmentation on the trestle images of the divided regions. Finally, based on the Speeded Up Robust Features and combined with the image, a panorama crack location output method was proposed. The system was used to test a section of a trestle in a coal mining industrial park, and the system showed that the method could efficiently and accurately identify and locate the crack damage.
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Xiong, C., Lian, S. & Chen, W. Detection and Location of Steel Structure Trestle Surface Cracks Based on Consumer-grade Camera System. KSCE J Civ Eng 27, 1150–1165 (2023). https://doi.org/10.1007/s12205-023-0522-1
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DOI: https://doi.org/10.1007/s12205-023-0522-1