Open Access

ARTICLE

Deep Learning-Based Automatic Detection and Evaluation on Concrete Surface Bugholes

Fujia Wei^1,2,*, Liyin Shen¹, Yuanming Xiang², Xingjie Zhang², Yu Tang², Qian Tan²

1 School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China
2 CMCU Engineering Co., Ltd., Chongqing, 400039, China

* Corresponding Author: Fujia Wei. Email:

Computer Modeling in Engineering & Sciences 2022, 131(2), 619-637. https://doi.org/10.32604/cmes.2022.019082

Received 01 September 2021; Accepted 05 November 2021; Issue published 14 March 2022

Abstract

Concrete exterior quality is one of the important metrics in evaluating construction project quality. Among the defects affecting concrete exterior quality, bughole is one of the most common imperfections, thus detecting concrete bughole accurately is significant for improving concrete exterior quality and consequently the quality of the whole project. This paper presents a deep learning-based method for detecting concrete surface bugholes in a more objective and automatic way. The bugholes are identified in concrete surface images by Mask R-CNN. An evaluation metric is developed to indicate the scale of concrete bughole. The proposed approach can detect bugholes in an instance level automatically and output the mask of each bughole, based on which the bughole area ratio is automatically calculated and the quality grade of the concrete surfaces is assessed. For demonstration, a total of 273 raw concrete surface images taken by mobile phone cameras are collected as a dataset. The test results show that the average precision (AP) of bughole masks is 90.8%.

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Keywords

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