An asphalt road crack image segmentation method based on a generative adversarial network

A technology of image segmentation and highway, applied in image analysis, biological neural network model, image data processing, etc., can solve the problems of large amount of labeled data, low recall rate, high model accuracy rate, etc., and achieve good generalization ability and robustness Effect

Inactive Publication Date: 2019-05-24
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

A common method is to divide the complete crack image into sub-images of equal size, and use a deep convolutional network (CNN) to classify the crack area and non-crack area of ​​the sub-image. Using CNN as a classification model can better extract image features. But the disadvantage is that a large amount of labeling data is required, and the accuracy of labeling data labeling is required to be high.
However, due to the reasons of the labeling personnel in the actual labeling, the blurred cracks in one image may be regarded as the cracked area, while the other image may be used as the normal area, which leads to the accuracy of the model when the data is blurred and the labeling is unclear. Higher rate, lower recall

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  • An asphalt road crack image segmentation method based on a generative adversarial network
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  • An asphalt road crack image segmentation method based on a generative adversarial network

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specific Embodiment

[0033] The implementation steps are as follows:

[0034] Step 1. Dataset preprocessing

[0035] Step 1.1: Build a model dataset. The size of the collected asphalt road crack image is 3040 pixels × 2048 pixels, and 100 images are randomly selected to form the original data set; the crack area is manually marked on the original data set, and the crack area is marked in black according to the actual size of the crack in the image. , the rest of the background area is marked in white, and the target data set is formed based on the relabeled data of the original data set.

[0036] Step 1.2: Image data generation. Due to the small amount of data in the original dataset in step 1.1, image data augmentation is required before training the model. Before training the model, the same random rotation transformation, random flip transformation, and random translation transformation are performed on the original data set and the target data set in step 1.1 to obtain the model training da...

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Abstract

The invention discloses an asphalt road crack image segmentation method based on a generative adversarial network. U-Net, CU-Net and FU-Net networks are used as a generator model G for generating an adversarial GAN models; GUNet, CUNet and FUNet contained in the generator model are respectively combined with three same binary classification network discriminator models D (Discriminative) to form UGAN, CUGAN and FUGAN models; mutual iteration competition optimization training is carried out through a generator and a discriminator, and finally trained generator model U-Net, CU-Net and FU-Net areused as crack image divider; U-Net, CU-Net and FU-Net models realize the image segmentation of the crack of the complex asphalt road. Compared with the prior art, the required training data set is less, the crack segmentation precision is higher, and the precision ratio and recall ratio are higher.

Description

technical field [0001] The invention relates to the technical field of image segmentation of asphalt road cracks, in particular to a method for segmenting cracks in asphalt road images using a deep learning generative confrontation network (GAN). Background technique [0002] Along with economic development, highway infrastructure construction plays an increasingly important role in the development of national economic construction. Regular maintenance and management of highways can reduce highway maintenance costs and traffic accidents. important parts of. Highway image crack segmentation is an important technology to assist highway monitoring and management automatic road surface disease detection. Many people have invested a lot of research on image crack segmentation technology through digital image processing technology. [0003] Due to the interference of random factors such as uneven illumination, random noise, blurred image gray levels, road signs and oil pollution ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/10G06N3/04G06N3/08
Inventor 彭博高子平李天瑞许伟强
Owner SOUTHWEST JIAOTONG UNIV
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