A CONVOLUTIONAL NEURAL NETWORK FOR PAVEMENT SURFACE CRACK SEGMENTATION USING RESIDUAL CONNECTIONS AND ATTENTION GATING

Conventional surface crack segmentation requires images manually labelled by a trained expert. It is a challenging task as cracks can vary in orientation and size, with some parts of cracks only being one pixel wide. Further, available training data for crack segmentation is sparse. In this work we propose to automate this annotation task, by introducing a fully convolutional U-Net based architecture for semantic segmentation of surface cracks which allows for the use of small datasets through a patch based training process. Our proposed configuration makes use of residual connections inside the convolutional blocks as well as including an attention based gating mechanism between the encoder and decoder section of this architecture, which only propagates relevant activations further. Using our proposed architecture we achieve new state of the art results in two different crack datasets, outperforming the previous best results in two metrics each.