Resin lens defect detection method based on convolutional neural network

A convolutional neural network and resin lens technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of inability to guarantee accuracy, low efficiency, and high labor costs

Active Publication Date: 2021-09-14
HARBIN UNIV OF SCI & TECH
View PDF6 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method is not only inefficient and has high labor costs, but also the judgment of defects is seriously a

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
  • Resin lens defect detection method based on convolutional neural network
  • Resin lens defect detection method based on convolutional neural network
  • Resin lens defect detection method based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] In order to make the technical solution described in the present invention clearer, further description will be given below in conjunction with the accompanying drawings and specific implementation. The accompanying drawings are only for illustrative purposes and should not be construed as limitations on this patent.

[0032] figure 1 A convolutional neural network-based defect detection method for resin lenses according to an embodiment of the present disclosure is shown. Such as figure 1 As shown, the method includes the following steps:

[0033] Step 1. Use a polarized black-and-white camera, use a white LED parallel light source, and use backlighting to obtain 2,000 images of resin lenses with 5 types of defects, such as pitting, scratches, bubbles, broken edges, and cracks, under dark field conditions. As the original image, the number of image pixels is 704×704;

[0034] Step 2. Carry out gradient sharpening and median filtering preprocessing operations to the ...

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 discloses a resin lens defect detection method based on a convolutional neural network. The method comprises the following steps: acquiring an image of a resin lens with defects; carrying out gradient sharpening and median filtering on the image to enhance detail information of the image; marking the position and category of the defect, and making a data set; constructing a convolutional neural network model, wherein the network model is based on an improved DarkNet53 as a backbone network, a multi-scale feature fused neck network and a multi-classification regression network as a detection head; inputting the data set into the network model for training; and inputting a to-be-detected picture into the trained network model for detection, and finally outputting information such as a defect category, a defect position and confidence, namely a final detection result. According to the invention, online detection of the defects of the resin lens can be realized, the generalization performance is good, and the detection efficiency and the detection precision are improved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a method for detecting defects of resin lenses based on a convolutional neural network. Background technique [0002] Due to its good optical properties, light weight, and low price, resin lenses are the most widely used in the glasses manufacturing industry and are a civilian product with huge demand. During the production process of resin lenses, defects such as pitting, scratches, spots, bubbles, chipping and inclusions may occur. Resin lenses with defects cannot be used because their optical properties are affected by defects. It is necessary to prevent products with defects from flowing into the market and causing greater losses to enterprises. Therefore, it is very necessary to perform defect detection on resin lenses. [0003] At present, the defect detection of resin lenses in the manufacturing industry is still mainly manual, that is, workers use their eyes to observe whe...

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): G06T7/00G06T5/00G06T5/20G06K9/62G06N3/04G06N3/08
CPCG06T7/0008G06T5/003G06T5/20G06N3/04G06N3/08G06T2207/20081G06T2207/20084G06T2207/20032G06F18/2431G06F18/253
Inventor 王洋文彩虹张光岳
Owner HARBIN UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap