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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

Segmentation Method of Concrete Small Cracks Based on UAV Images

Author(s): Yong Pan*, Wei Zou, Qiang Xv, Yan Zhao, Qifan Liang and Tong Zhao

Volume 17, Issue 5, 2024

Published on: 22 December, 2023

Article ID: e221223224774 Pages: 11

DOI: 10.2174/0126662558276323231129053808

Price: $65

Abstract

Introduction: Cracks are one of the major problems in modern concrete buildings, especially in locations that are difficult to map manually, such as bridges and high-rise buildings. Accurate analysis of unmanned aerial vehicle (UAV) images has become the key to determining whether a building needs maintenance.

Methods: Traditional image processing methods are easily interfered by high-frequency background. Neural network methods need fine datasets, which increase labor costs. Therefore, this paper proposes a segmentation algorithm based on UNet3+ network. After obtaining the UAV image, the rough location of the crack can be obtained by only rough labeling. And then, the sample balance can be carried out by clipping the target area. The UNet3+ network is used to train the processed datasets and extract the region of interest to ignore the non-target texture. Finally, the region of interest is further segmented by color clustering and edge detection methods.

Results: The proposed method can detect the cracks accurately. In all test images, the relative errors are less than 13%. Especially in test images whose width is less than 0.2mm, the maximum absolute error is only 0.0237 mm, which is completely acceptable in actual production. The proposed method has higher practicability in the detection of concrete crack images taken by UAV. The results show that the proposed method outperforms the cutting-edge method published in the journal "Sensor", when the background is complex.

Conclusion: The proposed method can segment and detect cracks effectively, which can remove the high-frequency interference region from the images.

Graphical Abstract

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