Open Access

ARTICLE

Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

Hyun Kyu Shin¹, Si Woon Lee², Goo Pyo Hong³, Lee Sael², Sang Hyo Lee⁴, Ha Young Kim^5,*
1 Architectural Engineering, Hanyang University, ERICA, Ansan, 15588, Korea
2 Department of Artificial Intelligence, Ajou University, Suwon, 16499, Korea
3 Division of Architecture and Civil Engineering, Kangwon National University, Samcheok, 25913, Korea
4 Division of Smart Convergence Engineering, Hanyang University, ERICA, Ansan, 15588, Korea
5 Graduate School of Information, Yonsei University, Seoul, 03722, Korea
* Corresponding Author: Ha Young Kim. Email:

Computers, Materials & Continua 2021, 66(3), 2493-2507. https://doi.org/10.32604/cmc.2021.014170

Received 03 September 2020; Accepted 22 September 2020; Issue published 28 December 2020

Abstract

The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that mostly occur in underground parking lots made of concrete structures. The deep learning-based object-detection method can replace conventional visual inspection methods. A faster region-based convolutional neural network (R-CNN) model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface defects. The defects were classified according to their type, and the learning of each exclusive model was ensured through test sets obtained from real underground parking lots. As a result, average precision scores of 37.76%, 36.42%, and 61.29% were obtained for paint peeling, leakage peeling, and leakage trace defects, respectively. Thus, this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.

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Keywords

Faster R-CNN; deep learning; defect detection; concrete structures

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