  Road pavement detection and segmentation are critical for developing
autonomous road repair systems. However, developing an instance segmentation
method that simultaneously performs multi-class defect detection and
segmentation is challenging due to the textural simplicity of road pavement
image, the diversity of defect geometries, and the morphological ambiguity
between classes. We propose a novel end-to-end method for multi-class road
defect detection and segmentation. The proposed method comprises multiple
spatial and channel-wise attention blocks available to learn global
representations across spatial and channel-wise dimensions. Through these
attention blocks, more globally generalised representations of morphological
information (spatial characteristics) of road defects and colour and depth
information of images can be learned. To demonstrate the effectiveness of our
framework, we conducted various ablation studies and comparisons with prior
methods on a newly collected dataset annotated with nine road defect classes.
The experiments show that our proposed method outperforms existing
state-of-the-art methods for multi-class road defect detection and segmentation
methods.
