Pavement crack detection method and apparatus based on deep learning and principle component analysis

Abstract

The invention provides a pavement crack detection method based on deep learning and principle component analysis. The method comprises the following steps: acquiring pavement images; performing gray-scale processing on the pavement images; then cutting each pavement image into W*V subimages each with a size of K*N pixels; manually selecting subimages comprising cracks and subimages not comprising cracks respectively as positive samples and negative samples which are taken together as a training set for training a convolutional neural network; establishing the convolutional neural network, training the convolutional neural network by use of the training set, detecting preprocessed pavement images to be analyzed by use of the trained convolutional neural network, and automatically extracting the subimages comprising the cracks; and performing crack type analysis, i.e., solving feature values and feature vectors by performing PCA calculation on distribution of the crack subimages, and determining crack types. According to the invention, automatic crack extraction is carried out on the pavement images by use of the convolutional neural network, pavement crack detection and crack type determination are realized, and a determining basis is provided for subsequent detection of the road crack types.

Description

technical field [0001] The invention relates to the field of pattern recognition, in particular to a pavement crack detection method and device based on deep learning and main cause analysis. Background technique [0002] In recent years, as the construction of highways, especially expressways, continues to accelerate, the problem of road maintenance, especially road cracks, has become increasingly serious. Among them, road cracks are mainly divided into transverse cracks, longitudinal cracks and network cracks. The formation of three types of cracks The mechanism is different, so different methods need to be used for repair. By detecting the type of crack, it can provide targeted repair advice and provide reference for further maintenance of the pavement. At present, manual detection is the mainstream method of road crack detection, which is not only time-consuming and inefficient, but also affected by the subjective feelings of the tester, which may easily lead to false de...

AI Technical Summary

Problems solved by technology

At present, manual detection is the mainstream method of road crack detection, which is not only time-consuming and inefficient, but also affected by the subjective feelings of the tester, which may easily lead to false detection and missed detection.

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