Abstract
Pavement damage is the main factor affecting road performance. Pavement cracking, a common type of road damage, is a key challenge in road maintenance. 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. First, the crack classification and detection model is applied to classify the cracks and obtain the detection confidence. Next, the crack segmentation network is applied to accurately segment the pavement cracks. By improving the feature extraction structure and optimizing the hyperparameters of the model, pavement crack classification and segmentation accuracy were improved. Finally, the length and width (for linear cracks) and the area (for alligator cracks) are calculated according to the segmentation results. Test results show that the recognition accuracy of the pavement crack identification method for transverse, longitudinal, and alligator cracks is 86.8%, 87.6%, and 85.5%, respectively. It is demonstrated that the proposed method can provide the category information for pavement cracks as well as the accurate positioning and geometric parameter information, which can be used directly for evaluating the pavement condition.
Highlights
Pavement distress is the main factor affecting road performance
Given the abovementioned problems in pavement crack identification, this paper proposes a method based on a deep convolutional neural network fusion model for pavement crack identification, which is applicable in many crack detection cases
With the deepening of network layers, crack features extracted by the deep convolutional neural network evolve from loworder features to high-order features. erefore, in order to perform a comprehensive study of crack features and improve the identification accuracy of pavement cracks, it is necessary to fully extract their features by increasing the convolutional network depth
Summary
And accurate detection of pavement damages is a crucial step in pavement maintenance. Pavement cracks will affect pavement appearance and driving comfort and can expand to cause pavement structural damage and shorten the overall service performance and life of the pavement [1, 2]. In 2014, Wang et al [9] proposed a pavement crack extraction method based on the valley bottom boundary; it uses a series of image processing algorithms to obtain the crack detection results. In 2015, Liang et al [10] proposed a pavement crack connection algorithm based on the Prim minimum spanning tree, which obtains the crack structure by filling the fracture
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