Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning

Pujin Wang, Jianzhuang Xiao, Ken’ichi Kawaguchi and Lichen Wang
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Pujin Wang: Department of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
Jianzhuang Xiao: Department of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
Ken’ichi Kawaguchi: Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan
Lichen Wang: Department of Civil Engineering, School of Civil Engineering, Tianjin University, Tianjin 300350, China

Sustainability, 2022, vol. 14, issue 6, 1-24

Abstract: To alleviate the workload in prevailing expert-based onsite inspection, a vision-based method using state-of-the-art deep learning architectures is proposed to automatically detect ceiling damage in large-span structures. The dataset consists of 914 images collected by the Kawaguchi Lab since 1995 with over 7000 learnable damages in the ceilings and is categorized into four typical damage forms (peelings, cracks, distortions, and fall-offs). Twelve detection models are established, trained, and compared by variable hyperparameter analysis. The best performing model reaches a mean average precision (mAP) of 75.28%, which is considerably high for object detection. A comparative study indicates that the model is generally robust to the challenges in ceiling damage detection, including partial occlusion by visual obstructions, the extremely varied aspect ratios, small object detection, and multi-object detection. Another comparative study in the F 1 score performance, which combines the precision and recall in to one single metric, shows that the model outperforms the CNN (convolutional neural networks) model using the Saliency-MAP method in our previous research to a remarkable extent. In the case of a large-area ratio with a non-ceiling region, the F 1 score of these two models are 0.83 and 0.28, respectively. The findings of this study push automatic ceiling damage detection in large-span structures one step further.

Keywords: ceiling damage detection; large-span structure; convolutional neural networks (CNN); object detection; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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