An End-to-End Pavement Crack Detection and Recognition Method Based on PCA

Abstract

The invention discloses a PCA-based end-to-end pavement crack detection and recognition method, which relates to the field of computer vision and includes the following steps: S1: Acquiring information about the road surface image I x The data set, and preprocess the data set; S2: classify the preprocessed data set, and calculate the background image I without cracks b , and get the road surface image I x with the background image I b difference image I, and build a training set and a test set in proportion; S3: use the training set image I to train the deep neural network model; S4: input the test set image I to the trained deep neural network model to obtain the image category probability, S5: Select the category label corresponding to the maximum probability as the result of image detection and recognition. This solution integrates the traditional detection and recognition process, which improves the efficiency and robustness of pavement crack detection.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a PCA-based end-to-end pavement crack detection and identification method. Background technique [0002] Whether it is asphalt or cement pavement, after a period of opening to traffic, cracks appear on the pavement due to the external environment, which brings great hidden dangers to the normal use of the pavement. Therefore, an effective detection and evaluation method is needed to detect and identify possible hidden dangers, so as to avoid potential hazards. Pavement crack detection refers to the process of first detecting whether there are cracks or defective parts in the pavement in the image, and then identifying the type of cracks. It involves two processes of detection and identification. This is a key problem in computer vision and is based on images. The basic technology of content recognition. Pavement crack detection can be used in road restoration, road condition moni...

AI Technical Summary

Problems solved by technology

However, due to the differences in road conditions and inconsistencies in various scenarios, this detection method is inefficient and has large errors or even fails, and it is impossible to determine the type of cracks.

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