Semi‐supervised learning based on convolutional neural network and uncertainty filter for façade defects classification. (20th October 2020) - Guo, Jingjing; Wang, Qian; Li, Yiting
Abstract: Developing a classifier to identify the defects from façade images using deep learning requires abundant labeled images. However, it is time‐consuming and uneconomical to label the collected images. Hence, it is desired to train an accurate classifier with only a small amount of labeled data. Therefore, this study proposes a semi‐supervised learning algorithm that uses only a small amount of labeled data for training, but still achieves high classification accuracy. In addition, based on the mean teacher algorithm, this study develops a novel uncertainty filter to select reliable unlabeled data for initial training epochs to further improve the classification accuracy. Validation experiments demonstrate that the proposed method can improve the model accuracy from 79.26% to 84.36% compared to the traditional supervised learning algorithm with 10% of labeled data in a dataset. From another perspective, compared to supervised learning algorithm, the proposed technique can help reduce the time and cost for preparing the labeled data.