A DCNN-based crack segmentation methodology is proposed by leveraging heterogeneous image fusion to alleviate image-related disturbances in intensity or range image data and mitigate uncertainties through cross-domain (i.e., intensity and range data domains) feature correlation. Intensity and range images are captured from concrete roadways and integrated through data fusion. Three encoder-decoder networks representing different patterns on exploiting the image data (i.e., fused raw image, raw range image, filtered range image, and raw intensity image) are proposed and compared to benchmarks. Experimental results demonstrate the proposed DCNN exploiting the fused raw image through an “extract-fuse” pattern achieves the most robust and accurate performance on crack segmentation among the implemented DCNNs.

通过利用异构图像融合来缓解强度或范围图像数据中与图像相关的干扰，并通过跨域（即强度和范围数据域）特征相关来减轻不确定性，提出了一种基于DCNN的裂缝分割方法。从混凝土路面捕获强度和距离图像，并通过数据融合将其整合。提出了三个在编码图像数据时代表不同模式的编码器/解码器网络（即，融合原始图像，原始范围图像，滤波范围图像和原始强度图像），并将其与基准进行比较。实验结果表明，所提出的DCNN通过“提取-熔断”模式利用融合后的原始图像实现了已实现的DCNN中最稳健，最准确的裂纹分割性能。