Façade 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.

中文翻译：

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基于元学习的卷积神经网络从不平衡数据集中进行立面缺陷分类

幕墙检查是一项常规而必要的维护任务，以确保建筑物的安全，功能和美观。传统的外观缺陷外观识别是危险，耗时且不充分的。基于图像数据集和深度学习算法，本研究开发了一种幕墙缺陷自动分类技术。提出了基于层的分类规则，对立面缺陷进行分类。为了解决缺陷类别之间数据大小不平衡的问题，应用了一种基于元学习的方法，该方法将权重重新分配给训练数据。实验表明，与传统方法相比，该方法通过将基本卷积神经网络（CNN）模型的分类准确率从71.43％提高到82，可以更好地处理不平衡数据集问题。