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DEEP LEARNING-BASED CRACK SEGMENTATION THROUGH HETEROGENEOUS IMAGE FUSION
DEEP LEARNING-BASED CRACK SEGMENTATION THROUGH HETEROGENEOUS IMAGE FUSION
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机译：基于深度学习的裂缝分割通过异构图像融合
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摘要
In an embodiment, a method for detecting cracks in road segments is provided. The method includes: receiving raw range data for a first image by a computing device from an imaging system, wherein the first image comprises a plurality of pixels; receiving raw intensity data for the first image by the computing device from an imaging system; fusing the raw range data and raw intensity data to generate fused data for the first image by the computing device; extracting a set of features from the fused data for the first image by the computing device; providing the set of features to a trained neural network by the computing device; and generating a label for each pixel of the plurality of pixels by the trained neural network, wherein a received label for a pixel indicates whether or not the pixel is associated with a crack.
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