Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Abstract

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance

“…In general, building durability can be increased through constant protection, repair, and maintenance activities [7,8]. e deterioration rate and degree differ among building components, with construction design, material, method, construction quality, and environment being the crucial influencing factors [9]. Several factors leading to building deterioration may be divided into the following categories: natural environment (temperature, relative humidity, sunshine, wind, and water), natural disasters (earthquakes and typhoons), and human factors (design, construction, users, management, and maintenance) [10][11][12][13].…”

“…Agyemang and Bader applied a CNN for detecting cracks on the building external walls and assessing the defects therein [3]. Perez et al also used CNNs to detect the building defects [9]. As shown in the related researches, VGG-16 and CAM are the commonly used methods in the application of building defect detection.…”

A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History‐Based Adaptive Differential Evolution Optimized Support Vector Machine

“…It is due to the sheer volume of surface area needed to be inspected, labeled, and reported. Moreover, as pointed out by Perez et al [19], the manual procedure also brings safety threat to human inspectors since certain concrete surfaces cannot be reached with ease, especially for those which are located at high/roof levels and narrow space. e current practice is also problematic due to the unavoidable inconsistency in inspecting outcomes.…”

“…e reason is that the assessment process is dependent of subjective judgment of human technicians [2,3]. erefore, project owners are increasingly seeking for fast, effective, and consistent tools to better structure condition assessment [19][20][21][22][23][24]. e assessment outcomes can also enhance communication between various stakeholders regarding the condition of the buildings.…”

“…Yoshitake et al [36] relied on binary image and color image analyses to detect bugholes distributed on sidewalls and tunnel-lining concrete. Perez et al [19] utilized the stateof-the-art deep convolutional neural networks (DCNN) for detecting and categorizing building surface defects. A novel instance-level recognition and quantification for concrete surface bughole based on the deep neural network has been recently developed by Wei et al [18]; this study demonstrates a great capability of machine learning in identifying concrete void surfaces accurately.…”