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Pavement multi-feature disease detection method and device based on multi-neural network combination

A detection method and detection device technology, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems of inability to obtain road information in real time, heavy workload of training models, etc., and achieve strong detection effect

Pending Publication Date: 2022-02-15
JIANGSU UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: In order to solve the problem that the general road surface disease detection method has a large workload in the early training model and cannot obtain road information in real time, the present invention provides a road surface multi-feature disease detection method and device based on the combination of multiple neural networks, and uses the improved confrontation generation network to reduce Real-time access to road information can reduce the workload of pre-training for road surface disease detection

Method used

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  • Pavement multi-feature disease detection method and device based on multi-neural network combination
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Embodiment 1

[0036] The invention provides a road surface multi-feature disease detection method based on the combination of multiple neural networks, such as figure 1 shown, including the following steps:

[0037] Before multi-featured road surface disease detection, the neural network needs to be trained and loaded into the Jetson nano system board. First, a small amount of road surface disease pictures are obtained through the camera mounted on the vehicle, and the size of the picture is adjusted to 300*300 for various road surfaces. Diseases are labeled for classification.

[0038] Table 1 Pavement disease category table:

[0039]

[0040] Such as figure 2 As shown in Fig. 1, build a generative confrontation network model, in which the encoder is the encoder module of the VAE variational autoencoder. After inputting the real road surface disease pictures into the encoder, the mean and variance of the real pictures are obtained, and then randomized according to the normal distribu...

Embodiment 2

[0053] The invention discloses a road surface multi-feature disease detection device based on the combination of multi-neural networks, such as Figure 4 Shown is the hardware design diagram, including power supply module, acquisition module, detection module and external modules.

[0054] The power supply module provides power supply support for the acquisition module, detection module and external modules.

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Abstract

The invention discloses a pavement multi-feature disease detection method based on multi-neural network combination, which comprises the following steps: generating a pavement disease picture by training a generative adversarial network, and expanding a data set to train a pavement disease detection model; loading the trained optimal pavement disease detection model into a system board for pavement disease detection; the road surface detection information is sent to a monitoring information platform through MQTT; and finally, the user looks up related pavement information through the platform. The method does not need to waste a large amount of time and manpower to collect and mark a data set, can rapidly detect the multi-feature diseases of the road surface, establishes a monitoring platform, and enables a user to check the road surface information anytime and anywhere.

Description

technical field [0001] The invention relates to the field of road surface disease detection, in particular to a multi-feature road surface disease detection method and device based on the combination of multiple neural networks. Background technique [0002] In recent years, my country's road construction has been continuously improved, and the transportation capacity has been continuously enhanced. However, with the growth of time, road facilities are gradually damaged, such as road structure damage, wear of zebra crossing white lines, cracks and potholes on the road surface, and rutting on the road surface. etc., bringing relatively serious social impacts and serious economic losses. Therefore, strengthening the planning and construction of road maintenance can effectively improve the quality of roads and avoid economic losses caused by road damage. At present, road disease inspection work is mainly based on manual inspection due to technical limitations, and only a small ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/10G06V10/764G06V20/40G06V10/80G06V10/82G06V10/774G06K9/62G06N3/04G06N3/08G06Q50/08G06Q10/00
CPCG06N3/088G06Q50/08G06Q10/20G06N3/045G06F18/2431G06F18/253G06F18/214
Inventor 赵新旭刘卫康王家晨张鸿鑫张博熠刘庆华
Owner JIANGSU UNIV OF SCI & TECH