MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network

Kisu Lee, Goopyo Hong, Lee Sael, Sanghyo Lee and Ha Young Kim
Additional contact information
Kisu Lee: Graduate School of Information, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
Goopyo Hong: Division of Architecture and Civil Engineering, Kangwon National University, 346 Jungang-ro, Samcheok-si, Gangwon-do 25913, Korea
Lee Sael: Department of Data Science, Ajou University, 206 World Cup-Ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16499, Korea
Sanghyo Lee: Division of Smart Convergence Engineering, Hanyang University ERICA, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do 15588, Korea
Ha Young Kim: Graduate School of Information, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea

Sustainability, 2020, vol. 12, issue 22, 1-14

Abstract: Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades.

Keywords: multi-class defect detection; building façade defect; deep learning; Faster R-CNN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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