Efficient Dataset Collection for Concrete Crack Detection With Spatial-Adaptive Data Augmentation - Jong-Hyun Kim; Jung Lee (IEEE Access, 2023)
In this paper, we propose a data augmentation technique based on Convolutional Neural Networks (CNN or ConvNet) training to efficiently obtain a dataset of images containing concrete cracks. Concrete cracks usually do not have a standardized shape and have complex patterns, making it difficult to obtain images of them, and there is a risk of exposure to dangerous situations when securing data. Therefore, in this paper, we efficiently address the difficulty of dataset collection by using a data augmentation technique based on learning the direction and thickness of cracks, which is cost-effective and time-efficient. Moreover, to improve efficiency, we introduce a method of adaptively handling crack data by constructing a quadtree based on the presence of cracks. To confirm the extent of the improvement in accuracy, we conducted experiments applying the crack detection algorithm to various scenes, and the accuracy was improved in all scenes when measured by IoU (Intersection over union) accuracy. When the algorithm was performed without augmenting the crack data, the false detection rate was about 25%. However, when we augmented the data using our method, the false detection rate significantly decreased to 3%.