  The acquisition and evaluation of pavement surface data play an essential
role in pavement condition evaluation. In this paper, an efficient and
effective end-to-end network for automatic pavement crack segmentation, called
RHA-Net, is proposed to improve the pavement crack segmentation accuracy. The
RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid
attention blocks into the encoder-decoder architecture. The ResBlocks are used
to improve the ability of RHA-Net to extract high-level abstract features. The
hybrid attention blocks are designed to fuse both low-level features and
high-level features to help the model focus on correct channels and areas of
cracks, thereby improving the feature presentation ability of RHA-Net. An image
data set containing 789 pavement crack images collected by a self-designed
mobile robot is constructed and used for training and evaluating the proposed
model. Compared with other state-of-the-art networks, the proposed model
achieves better performance and the functionalities of adding residual blocks
and hybrid attention mechanisms are validated in a comprehensive ablation
study. Additionally, a light-weighted version of the model generated by
introducing depthwise separable convolution achieves better a performance and a
much faster processing speed with 1/30 of the number of U-Net parameters. The
developed system can segment pavement crack in real-time on an embedded device
Jetson TX2 (25 FPS). The video taken in real-time experiments is released at
https://youtu.be/3XIogk0fiG4.
