Bounding-box object augmentation with random transformations for automated defect detection in residential building façades

Abstract

This study proposes a novel bounding-box object augmentation (BoxAug) method to improve the performance of deep learning models in detecting defects in residential building façades. The most significant characteristic of the method is that it augments objects in images, rather than augmenting images, to solve the data imbalance problem. Moreover, it employs the bounding-box form for object detection, instead of the segmentation mask form. To evaluate the method, 7635 images obtained using unmanned aerial vehicles were utilized as the original training dataset. The faster region-based convolutional neural network model trained with the augmented training dataset using the method exhibited better performance than the model trained with the original dataset. Particularly, the class with the least objects in the original dataset displayed a markedly improved performance. Thus, the method can serve as an auxiliary method for effectively augmenting real-world image datasets with an unbalanced number of objects.