ARF-Crack: rotation invariant deep fully convolutional network for pixel-level crack detection

Abstract

Autonomous detection of structural defect from images is a promising, but also challenging task to replace manual inspection. With the development of deep learning algorithms, several studies have adopted deep convolutional neural networks (CNN) or fully convolutional networks (FCN) to detect cracks in pixel-level. However, a fundamental property of cracks, that they are rotation invariant, has never been exploited. Although the rotation-invariant property can be implicitly learned by data augmentation, the network needs more parameters to learn features of different orientations and thus tend to overfit the training data. In this study, a rotation-invariant FCN called ARF-Crack is proposed that utilizes the rotation-invariant property of cracks explicitly. The architecture of a state-of-the-art FCN called DeepCrack for pixel-level crack detection is adopted and revised where active rotating filters (ARFs) are used to encode the rotation-invariant property into the network. The proposed ARF-Crack is evaluated on several benchmark datasets including concrete cracks, pavement cracks and corrosion images. The experimental results show that the proposed ARF-Crack requires less number of network parameters and achieves the highest average precision values for all the benchmark datasets compared to other approaches. The proposed ARF-Crack has the potential of detecting other rotation-invariant defects.