First, FCN is trained by feeding multiple types of cracks to semantically identify and segment pixel-wise cracks at different scales. Therefore, this research implements a novel deep learning technique named fully convolutional network (FCN) to address this problem. But existing crack detections are of (high specificity) low generality and inefficient, in terms that conventional approaches are unable to identify and measure diverse cracks concurrently at pixel level. To improve the efficiency of crack inspection, advanced computer vision-based techniques have been utilized to detect cracks automatically at image level and grid-cell level. ![]() However, the current manual crack description method is time consuming and labor consuming. The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures.
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