Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Automobile surface damage classification method and device based on deep learning

A car surface and deep learning technology, applied in image analysis, image data processing, instruments, etc., can solve complex and changeable appearance scratches and other problems

Active Publication Date: 2016-11-16
高前文
View PDF4 Cites 72 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The exterior scratches of the car are complex and changeable, and are easily affected by external interference such as light and occlusion

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Automobile surface damage classification method and device based on deep learning
  • Automobile surface damage classification method and device based on deep learning
  • Automobile surface damage classification method and device based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0086] All the features disclosed in this specification, or all disclosed methods or steps in the process, except for mutually exclusive features and / or steps, can be combined in any manner.

[0087] Any feature disclosed in this specification, unless specifically stated, can be replaced by other equivalent or equivalent alternative features. That is, unless otherwise stated, each feature is just one example of a series of equivalent or similar features.

[0088] Deep learning is a machine learning theory that combines low-level features to form more abstract high-level representation attribute categories or features to discover distributed feature representations of data. It can be divided into supervised learning and unsupervised learning. Convolutional neural network is a deep learning model under supervised learning. It is a non-fully connected neural network structure that can automatically learn target features containing a large amount of data. , It has certain robustness t...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to the field of image detection, and especially relates to an automobile surface damage classification method and device based on deep learning. According to the method and the device, the classification method and device are provided for the problems in the prior art. Characteristic learning and classification are carried out on input to-be-detected images. Specifically, candidate areas are extracted from the to-be-detected images to by employing an area selective search algorithm, and location information of the candidate areas are recorded; the to-be-detected images are input into a characteristic diagram extraction network model without an output layer, thereby extracting the characteristic vectors of the candidate areas of the to-be-detected images; the characteristic vectors of the candidate areas are input into an SVM classifier to find target characteristic vectors; the locations of the corresponding candidate areas on the to-be-detected images, namely, the target areas of the to-be-detected images, are found according to the locations of the target characteristic vectors in the characteristic diagram; and the target areas of the to-be-detected images are input into an optimum classification network model, and the probabilities of the areas on damage levels are output.

Description

Technical field [0001] The invention relates to the field of image detection, in particular to a method and device for classification of car surface damage based on deep learning. Background technique [0002] In recent years, with the continuous development of my country's urbanization, the number of vehicles per capita in my country has continued to increase. The survey shows that as of 2015, the total number of cars in the country has exceeded 170 million. At the same time, the safety problems caused by cars are also increasing. After an accident, no matter whether it is a collision between motor vehicles or between a vehicle and a fixed object, it will leave marks on the vehicle. These traces will seriously affect the appearance and use of the car, and different types of traces require different maintenance costs. Therefore, these traces need to be evaluated. At present, the assessment of scratches on the appearance of automobiles mainly relies on the subjective judgment of...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0006G06T2207/30248G06T2207/20081G06F18/2411
Inventor 史方樊强王标
Owner 高前文
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com