Open Access

ARTICLE

Wall Cracks Detection in Aerial Images Using Improved Mask R-CNN

Wei Chen¹, Caoyang Chen^1,*, Mi Liu¹, Xuhong Zhou², Haozhi Tan³, Mingliang Zhang⁴

1 College of Civil Engineering, Changsha University of Science and Technology, Changsha, 410114, China
2 College of Civil Engineering, Chongqing University, Chongqing, 730000, China
3 Department of Civil and Environmental Engineering, University of Auckland, Auckland, 1010, New Zealand
4 Hunan Construction Engineering Group Co. Ltd., Changsha, 410004, China

* Corresponding Author: Caoyang Chen. Email:

Computers, Materials & Continua 2022, 73(1), 767-782. https://doi.org/10.32604/cmc.2022.028571

Received 12 February 2022; Accepted 23 March 2022; Issue published 18 May 2022

Abstract

The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency. The proposed method is based on Unmanned Aerial Vehicle (UAV) and computer vision technology. First, a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions. Second, the average crack detection precisions of different methods including the Single Shot MultiBox Detector, You Only Look Once v3, You Only Look Once v4, Faster Regional Convolutional Neural Network (R-CNN) and Mask R-CNN methods were compared. Then, the Mask R-CNN method with the best performance and average precision of 0.34 was selected. Finally, based on the characteristics of cracks, the utilization ratio of Mask R-CNN to the underlying features was improved so that the average precision of 0.9 was achieved. It was found that the positioning accuracy and mask coverage rate of the proposed Mask R-CNN method are greatly improved. Also, it will be shown that using UAV is safer than manual detection because manual parameter setting is not required. In addition, the proposed detection method is expected to greatly reduce the cost and risk of manual detection of building exterior wall cracks and realize the efficient identification and accurate labeling of building exterior wall cracks.

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Keywords

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