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Highway pavement disease identification method based on improved YOLOv5 model

A technology for highway and disease identification, applied in the field of intelligent transportation, can solve problems such as insufficient identification, and achieve the effect of increasing network depth, accurate detection and classification

Pending Publication Date: 2022-05-06
SOUTHEAST UNIV
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Problems solved by technology

[0002] With the increasing traffic flow on expressways and the existence of some illegal and overloaded vehicles, various pavement diseases have begun to appear on many expressways. The traditional manual-based pavement disease identification is far from meeting the needs of a large number of expressway maintenance. , based on the road surface detection vehicle to realize automatic highway pavement information collection and the target detection algorithm based on deep learning and computer vision provides new solutions and ideas for automatic fast collection and identification of highway pavement diseases

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  • Highway pavement disease identification method based on improved YOLOv5 model
  • Highway pavement disease identification method based on improved YOLOv5 model
  • Highway pavement disease identification method based on improved YOLOv5 model

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[0022] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0023] The improved YOLOv5 expressway pavement disease recognition method provided by the present invention comprises the following steps:

[0024] S1: Build a YOLOv5 target detection model based on different feature extraction networks, and perform feature extraction on highway road images:

[0025] S1-1: Build the Efficientnet-YOLOv5 model.

[0026] The resolution of the input image of the EfficientNet network (as shown in Table 1) is 224×224. In the first Stage layer, a convolution operation with a size of 3×3 convolution kernel is performed and the number of channels of the output feature map is increased. to 32; in the second Stage layer to the eighth Stage laye...

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Abstract

The invention discloses an expressway pavement disease recognition method based on an improved YOLOv5 model, and the method comprises the steps: constructing a target detection model suitable for expressway pavement disease recognition through the improvement of a model feature extraction network and the redesign of an anchor frame of a model on the basis of an existing mature YOLOv5 model. Through close combination of the deep learning network, the method is applied to the field of expressway pavement disease recognition, so that the disease recognition efficiency can be greatly improved, and technical support is provided for expressway maintenance.

Description

technical field [0001] The patent of the present invention relates to the field of intelligent transportation and intelligent high-speed research, and specifically relates to a method for identifying road surface defects on highways based on the improved YOLOv5 model. Background technique [0002] With the increasing traffic flow on expressways and the existence of some illegal and overloaded vehicles, various pavement diseases have begun to appear on many expressways. The traditional manual-based pavement disease identification is far from meeting the needs of a large number of expressway maintenance. , the realization of fully automatic highway pavement information collection based on road surface inspection vehicles and the target detection algorithm based on deep learning and computer vision provide new solutions and ideas for fully automatic and fast collection and identification of highway pavement diseases. In view of this, the present invention builds YOLOv5 models w...

Claims

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Application Information

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IPC IPC(8): G06V20/10G06K9/62G06N3/04G06N3/08G06V10/40G06V10/774G06V10/82G06V10/762
CPCG06N3/08G06N3/045G06F18/23213G06F18/214
Inventor 赵池航覃晓明毛迎兵刘洋吴加伦
Owner SOUTHEAST UNIV
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