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.
Highlights
A building should exhibit good performance in supporting the activities of its occupants
In this study, we propose a multiclass object detection model capable of distinguishing various types of defects occurring in residential building façades by employing a faster region proposal convolutional neural network (Faster regions with CNN features (R-CNN))
We developed a model that is capable of multiclass defect detection from real-world image data obtained from various building façades by employing the Faster R-CNN structure as the base model, as described above
Summary
A building should exhibit good performance in supporting the activities of its occupants. Among the various types of buildings, residential buildings should perform well because occupants spend most of their time in them [1]. Continuous exposure to poor environmental conditions during a long service life accelerates aging relatively faster compared with other building components [5]. This phenomenon is eventually manifested in various types of defects on the building façade [6]. If various defects in the building façade are ignored, they may result in shortening the service life, damage to appearance, and increased maintenance costs [7]. There is a need for a method to effectively monitor defects in the maintenance phase and actively respond to the occurrence of the defects [8,9]
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