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Road disease detection system and method based on deep learning of image recognition

A road disease and deep learning technology, applied in the field of intelligent transportation, can solve the problems of excessive calculation amount, high image noise influence, poor detection effect, etc., to achieve high prediction accuracy and efficiency, good generalization ability, and calculation amount small effect

Active Publication Date: 2021-09-24
四川九通智路科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, there are some problems in these two methods: firstly, the processing logic of these two methods is not perfect when dealing with the actual road surface features, so the detection effect is often not good in actual performance; secondly, the LBP operator is not good in the flat image area. It is not very robust and is highly affected by image noise; moreover, the Gabor operator has a large amount of calculation when extracting image features.

Method used

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  • Road disease detection system and method based on deep learning of image recognition
  • Road disease detection system and method based on deep learning of image recognition
  • Road disease detection system and method based on deep learning of image recognition

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Embodiment 1

[0048] As a specific implementation of the road disease detection system based on image recognition deep learning of the present invention, the disclosed system includes an image processing module, an image detection module, an image segmentation module, and an image classification module. Specifically, the image processing module uses Preprocessing is performed on the collected image of the road to be detected. The image of the road to be detected includes a picture of road damage on the road surface and label data of related road damage, and the preprocessed image is sent to the image detection module.

[0049] And the image detection module extracts the part belonging to the road pavement from the image preprocessed by the image processing module, and sends it to the image segmentation module and image The classification module is used for subsequent segmentation of disease forms and classification of disease categories.

[0050] The image segmentation module uses the train...

Embodiment 2

[0053] As a specific implementation of the road disease detection method based on deep learning of image recognition in the present invention, such as figure 1 , the disclosed road disease detection method includes a sample image acquisition step, a sample image preprocessing step, a sample labeling step, a model training step and a road disease detection step.

[0054] Specifically, the sample image collection step is to collect a number of different roads and images of road surface conditions including various road diseases to form a sample image set, that is, to establish an atlas with specific conditions such as the location and type of road diseases as a standard database; Preferably, the original picture size of the road condition image is 608x608 pixels.

[0055] The sample image preprocessing step is to perform cropping, flipping, and brightness / contrast / hue conversion processing on the road condition images of different roads and various road diseases contained in the...

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Abstract

The invention belongs to the technical field of intelligent transportation, and specifically relates to a road disease detection system and method based on deep learning of image recognition. The system includes an image processing module, an image detection module, an image segmentation module, and an image classification module. The method includes sample image acquisition steps, The sample image preprocessing step, sample labeling step, model training step and road defect detection step are a road defect detection technology based on deep learning to detect, classify and segment road images.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, and in particular relates to a road disease detection system and method based on deep learning of image recognition. Background technique [0002] The road structure layer can be divided into surface layer, base layer and soil foundation, and the base layer can be divided into cushion layer (subbase layer) and base layer; the main function of the roadbed is to bear the weight of the road structure layer and the load road surface, which is the soil layer; the cushion layer is the road surface The bottom layer is used to drain water, diffuse the stress of the base layer and transfer the stress to the subgrade; the base layer is mainly load-bearing, and spread the stress of the surface layer to the cushion layer; the surface layer is mainly to improve driving conditions and protect the base layer of the pavement. That is, the subgrade is a geotechnical structure excavated or fille...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/187G06K9/32G06K9/38G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06T7/11G06T7/187G06N3/08G06T2207/10004G06T2207/10024G06T2207/20081G06T2207/20084G06T2207/30132G06V10/28G06V10/25G06V10/44G06N3/045G06F18/241
Inventor 寇世豪郑武张蓉邓承刚杨海涛
Owner 四川九通智路科技有限公司