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

Aluminum material image defect detection method based on self-adaptive anchor frame

A defect detection and self-adaptive technology, applied in the field of computer vision and defect detection, can solve the problems of inflexible detection methods and poor detection methods

Active Publication Date: 2020-12-15
XI AN JIAOTONG UNIV
View PDF8 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide an aluminum image defect detection method based on an adaptive anchor frame to solve the problems that the current detection method is not effective and the detection means are not flexible enough

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
  • Aluminum material image defect detection method based on self-adaptive anchor frame
  • Aluminum material image defect detection method based on self-adaptive anchor frame
  • Aluminum material image defect detection method based on self-adaptive anchor frame

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0082] Below in conjunction with accompanying drawing, the present invention is further described:

[0083] see figure 1 , the present invention comprises the following steps:

[0084] Step 101, using a camera to acquire image data or directly uploading image data as image input.

[0085] Step 102, perform s times downsampling operation on the original image (W×S) to obtain an image of size (W / s)×(H / s).

[0086] Step 103, using ResNeXt-101 combined with group convolution and deformable convolution as the backbone network for feature extraction, and processing the original input image through a convolution layer with a convolution kernel of 7×7 and a batch normalization layer Finally, it is divided into 64 groups and entered into Conv2-Conv5. The group convolution can prevent over-fitting of a specific data set without changing the parameter amount, so as to achieve a more accurate effect.

[0087] Step 104, input the features extracted in step 103 into the attention module ...

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 provides an aluminum material image defect detection method based on a self-adaptive anchor frame to solve the problems that a current defect detection method is not flexible enough, lowin detection precision and the like. Firstly, ResNeXt-101 using packet convolution and deformable convolution ideas is adopted as a backbone network, a feature enhancement module containing an attention mechanism is integrated into the backbone network, and then the feature enhancement module is sent into a feature pyramid network for multi-scale feature fusion, so that the defect detection precision is improved; secondly, a self-adaptive anchor frame neural network is used, corresponding anchor frame parameters are learned automatically according to defect features, and the precision of anchor frame positioning detection is improved; then, a cascade network structure is adopted in the frame prediction stage, and the problem that the precision in the training stage is not matched with that in the prediction stage is solved; and finally, the detection precision of the defects with large shape difference and small target defects is greatly improved, the overall precision of aluminum material image defect detection is relatively high, and the method has relatively high application value in the field of defect detection.

Description

technical field [0001] The invention belongs to the field of computer vision and defect detection, mainly adopts the idea of ​​deep learning, and specifically relates to an aluminum material image defect detection method based on an adaptive anchor frame. Background technique [0002] Aluminum profile is the pillar industry of all industrialized countries, with the characteristics of high strength, light weight, wear resistance, good decoration, environmental protection and economy, more and more architects start to use aluminum profile as building material. Therefore, quality has become a key evaluation criterion for aluminum profiles. However, in actual production, due to the impact of the production process, the aluminum surface may have defects such as non-conductivity, scratches, coarse grains, exposed boards, blisters, pits, protruding particles, exposed board corners, paint marks and variegated colors. . Due to the harsh environment and high cost, non-contact inspec...

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
IPC IPC(8): G06T7/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06T7/0002G06N3/084G06T2207/10004G06T2207/20016G06T2207/20081G06T2207/20084G06T2207/30168G06V10/44G06N3/045G06F18/253
Inventor 田智强董靓杰王欢许博郑尧月
Owner XI AN JIAOTONG UNIV
Features
  • Generate Ideas
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More