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.

道路路面检测和分割是开发自主道路修复系统的关键，然而，由于道路路面图像的纹理简单、缺陷几何的多样性以及类别之间形态的模糊性，同时执行多类别缺陷检测和分割的实例分割方法具有挑战性。我们提出了一种新颖的端到端方法，用于多类别道路缺陷检测和分割，该方法包括多个空间和通道注意力块，可用于学习跨空间和通道维度的全局表示。通过这些注意力块，可以学习到更加全局化的道路缺陷的形态信息（空间特征）和图像的颜色和深度信息。为了证明我们提出的框架的有效性，我们在一个新收集的用于九种道路缺陷类别注释的数据集上进行了各种消融研究和与先前方法的比较实验。实验结果显示，我们提出的方法优于现有的多类别道路缺陷检测和分割方法的最新方法。