![]() ![]() Finally, the length and width (for linear cracks) and the area (for alligator cracks) are calculated according to the segmentation results. By improving the feature extraction structure and optimizing the hyperparameters of the model, pavement crack classification and segmentation accuracy were improved. ![]() Next, the crack segmentation network is applied to accurately segment the pavement cracks. First, the crack classification and detection model is applied to classify the cracks and obtain the detection confidence. In order to achieve an accurate crack classification, segmentation, and geometric parameter calculation, this paper proposes a method based on a deep convolutional neural network fusion model for pavement crack identification, which combines the advantages of the multitarget single-shot multibox detector (SSD) convolutional neural network model and the U-Net model. ![]() Pavement cracking, a common type of road damage, is a key challenge in road maintenance. Pavement damage is the main factor affecting road performance. ![]()
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