Concrete Roadway Crack Segmentation Using Encoder-Decoder Networks With Range Images

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.