Abstract Recently, researchers have utilized DCNN for pixel-wise crack classification through semantic segmentation. Nevertheless, some issues in current DCNN-based roadway crack segmentation are yet to be fully addressed. For example, image pre-processing techniques are often required to eliminate the surface variations in range images, which may bring uncertainties due to subjective parameter selection; besides, disturbances from many non-crack patterns such as pavement grooves can deteriorate the crack segmentation performance, which remains a challenge for current DCNN-based methodologies. This paper proposes a methodology based on encoder-decoder networks to achieve pixel-wise crack classification performance on laser-scanned range images, under the disturbance of surface variations and grooved patterns in concrete pavements. The raw range data is directly applied in this methodology without any pre-processing. A comparative study is performed to determine the optimal architecture layout among twelve proposed candidates. Meanwhile, the influence of residual connections on DCNN performance is investigated and demonstrated.

中文翻译：

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使用带有距离图像的编码器-解码器网络进行混凝土路面裂缝分割

摘要 最近，研究人员利用 DCNN 通过语义分割进行像素级裂纹分类。然而，当前基于 DCNN 的道路裂缝分割中的一些问题尚未完全解决。例如，通常需要图像预处理技术来消除距离图像中的表面变化，这可能会因主观参数选择而带来不确定性；此外，来自许多非裂缝模式（例如路面凹槽）的干扰会降低裂缝分割性能，这仍然是当前基于 DCNN 的方法的挑战。本文提出了一种基于编码器-解码器网络的方法，在混凝土路面表面变化和凹槽图案的干扰下，在激光扫描距离图像上实现像素级裂缝分类性能。原始范围数据直接应用于此方法中，无需任何预处理。进行比较研究以确定十二个提议的候选者之间的最佳架构布局。同时，研究并证明了残差连接对 DCNN 性能的影响。