Pavement crack segmentation and recognition method based on deep learning

Abstract

The invention discloses a pavement crack segmentation and recognition method based on deep learning. The pavement crack segmentation and recognition method comprises the following steps: firstly, manually labeling an acquired color crack sample image to obtain a crack label image; performing sub-image segmentation of the same size and position on the two types of images, marking whether the sub-images contain cracks, training a U-Net neural network by using the marked sub-images, and training the decision network by using the results of the last two layers of the U-Net neural network as the input of the decision network; and finally, obtaining a trained network model, and performing non-overlapping sliding window detection and classification on the to-be-identified image so as to obtain the segmentation and identification result of the image. According to the pavement crack segmentation and recognition method, the crack image is segmented, and whether the image contains cracks can be accurately identified, so that the purposes of efficient, accurate and low-cost pavement crack segmentation and identification can be achieved.

Description

technical field [0001] The invention belongs to the field of image processing and computer vision, and in particular relates to a method for segmenting and identifying pavement cracks based on deep learning. Background technique [0002] As we all know, cracks are a potential threat to the safety of bridges and roads, seriously affecting the safety of transportation. To maintain good bridge and road conditions, cracks should be located and repaired in a timely manner. Therefore, precise location and detection of cracks plays a vital role. [0003] At present, the detection of cracks at home and abroad mainly adopts the method of manual detection, that is, technicians directly detect and record with the help of some simple instruments or naked eyes. However, this method has obvious disadvantages, such as requiring a large amount of labor costs, low detection efficiency, and false detections caused by manual detection. [0004] With the rise of deep learning, applying deep ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the shortcomings of high labor cost and low detection efficiency in pavement crack detection, the present invention proposes a pavement crack segmentation and identification method based on deep learning. Thi

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