Method, system and device for constructing few-sample industrial image defect detection model
A technology for detecting models and images, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as industrial image defects, and achieve the effect of improving detection accuracy and speed
Pending Publication Date: 2022-07-22
GUANGZHOU UNIVERSITY
View PDF0 Cites 1 Cited by
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
[0005] The purpose of the present invention is to provide a method, system and device for building a few-sample industrial image defect detection model, aiming at solving the problem of building a few-sample industrial image defect detection model
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 moreImage
Smart Image Click on the blue labels to locate them in the text.
Smart ImageViewing Examples
Examples
Experimental program
Comparison scheme
Effect test
Embodiment 1
[0174] The embodiment of the present invention provides a few-sample industrial image defect detection model construction device for image segmentation, such as Figure 7 As shown, it includes: a memory 70, a processor 72, and a computer program stored in the memory 70 and executable on the processor 72, and the computer program implements the steps in the above method embodiments when the computer program is executed by the processor.
Embodiment 2
[0176] An embodiment of the present invention provides a computer-readable storage medium, where an implementation program for information transmission is stored thereon, and the program is executed by the processor 72 to implement the steps in the foregoing method embodiments.
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
Login to View More Abstract
The invention discloses a few-sample industrial image defect detection model construction method, system and device. The method comprises the following steps: dividing an industrial image data set into a training set and a test set; preprocessing the training set and the test set to enhance the image contrast; performing random vertical overturning and horizontal overturning on the preprocessed training set to obtain training sample data; inputting training sample data into the segmentation network for training, and completing first-step construction of a detection model; correcting the mask image to obtain an industrial image defect area, and completing second-step construction of the detection model; splicing the mask pattern and the industrial image defect area to obtain a dual-channel feature pattern, and inputting the dual-channel feature pattern into a decision network for training to complete third-step construction of a detection model; and evaluating the detection model according to the defect classification result of the industrial images of different defect types in the test set. According to the invention, the detection precision and speed of the defect image can be improved, and the constructed model is evaluated.
Description
technical field [0001] The invention relates to the field of constructing a few-sample industrial image defect detection model, in particular to a method, system and device for constructing a few-sample industrial image defect detection model. Background technique [0002] At present, the methods of industrial image defect detection can be divided into two categories: methods based on traditional machine vision and methods based on deep learning. [0003] The traditional machine vision method is mainly based on the use of threshold segmentation, morphological processing, wavelet transform, edge detection and other algorithms to achieve defect detection. In deep learning, convolutional neural networks are mainly used to extract features from images. For example, classification networks, segmentation networks, and target detection networks are used to achieve defect detection. The commonly used models are: ResNet, YOLO, U-Net, etc. [0004] The method based on traditional mac...
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
Login to View More IPC IPC(8): G06T7/00G06T5/30G06K9/62G06N3/04G06N3/08G06V10/774G06V10/764G06V10/28G06V10/82
CPCG06T7/0004G06T5/30G06N3/08G06T2207/20081G06T2207/20084G06T2207/30108G06N3/048G06N3/045G06F18/241G06F18/214
Inventor 彭凌西谢翔彭绍湖林煜桐林焕然
Owner GUANGZHOU UNIVERSITY
PatSnap Eureka turns technology decisions into work you can execute. Powered by our Innovation Knowledge Graph, it runs expert workflows across engineering, life sciences, materials and intellectual property. Get your review-ready output in minutes.
