Online learning system for defect detection
Through the online learning system, deep learning models and multi-field image processing, combined with point cloud images, automated defect detection is achieved, solving the problems of low degree of defect detection automation and limited detection accuracy in the existing technology, and improving the comprehensiveness and accuracy of detection.
CN119723256APending Publication Date: 2025-03-28NUCTECH CO LTD +2
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Patent Information
- Application Number
- CN202411853501.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-16
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
Existing defect detection methods require manual participation, low degree of automation, and the detection results are usually targeted at some type of defect, with limited detection accuracy.
Method used
It provides an online learning system, through the server training of the deep learning model, the client acquires the captured images and point cloud images under multiple fields of view, performs defect detection, and combines the position information mapping relationship between the captured images and point cloud images to determine the defect detection results.
Benefits of technology
It realizes automated defect detection, improves the comprehensiveness and accuracy of defect detection, and can provide accurate detection results for different defect types.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention provides an online learning system for defect detection, and the system comprises a server which is used for training a deep learning model through employing a plurality of sample shooting images, and obtaining a target detection model; the client is used for acquiring shot images and point cloud images of a detection object in a plurality of view fields; processing the point cloud image to obtain first defect information; a target detection model is utilized to process the transformed shot image to obtain second defect information, and the first defect information and the second defect information aim at different defect types; and determining a defect detection result according to the first defect information and the second defect information on the basis of a mapping relationship between the position information of the transformed shot image and the point cloud image.
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