Fast Crack Segmentation with Depth-to-Space Operator for Pavement Maintenance

The quality of a city's infrastructure drives socioeconomic development. Specifically, it is important to streamline pavement quality monitoring to improve transportation. However, crack segmentation is a computational challenging problem that requires a fast response. In this paper, we propose a Fully Convolutional Network (FCN) for pavement crack segmentation using depth-to-space operation in the decoder and direct connections between the encoder and decoder layers to improve segmentation performance. This approach reduces the number of layers in the decoder. Consequently, training and inference computational costs are reduced. We tested our model on public datasets for comparison with fast state-of-the-art methods. Our model yielded better performance with lower computational costs, reaching real-time segmentation at the rate of 11 frames-per-second. Besides, we introduce a new dataset called CrackIPN as a benchmark that has four times more images and greater image diversity than commonly used datasets.