Façade defects classification from imbalanced dataset using meta learning‐based convolutional neural network

Abstract

AbstractFaçade inspection is a regular but necessary maintenance task to ensure the safety, functioning, and aesthetics of a building. Traditional visual identification of façade defects is dangerous, time‐consuming, and insufficient. Based on an image dataset and deep learning algorithms, an automatic façade defects classification technique is developed in this research. A layer‐based categorization rule is proposed to categorize façade defects. To handle the problem of imbalanced data size among defect classes, a meta learning‐based method is applied, which reassigns weights to the training data. Experiments demonstrated that the proposed method had a stronger capacity to deal with the imbalanced dataset problem comparing with previous methods by improving the classification accuracy from 71.43% of a basic convolutional neural network (CNN) model to 82.86% of a meta learning‐based CNN model.