Zipper head appearance defect detection system and method
By using a convolutional neural network model to perform target detection and multi-scale feature extraction on zipper head images and constructing an associated classification feature matrix, the problems of inconsistency in manual detection results and visual fatigue are solved, thereby improving the accuracy and production efficiency of zipper head crack detection.
CN121837686APending Publication Date: 2026-04-10HUZHOU JUNCHENG ZIPPER CO LTD
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUZHOU JUNCHENG ZIPPER CO LTD
- Filing Date
- 2024-01-09
- Publication Date
- 2026-04-10
Smart Images
Figure CN121837686A_ABST
Abstract
The invention relates to the field of defect detection, and particularly discloses a zipper head appearance defect detection system and a zipper head appearance defect detection method. Secondly, performing target detection on the to-be-detected zipper head image acquired by the camera and a zipper head filter based on a convolutional neural network model to obtain a zipper head region-of-interest feature vector; pooling the to-be-detected zipper head image through a convolutional neural network with a multi-scale convolutional structure to obtain a zipper head detection feature matrix, and further, performing associated feature coding on the zipper head region-of-interest feature vector and the zipper head detection feature matrix, and obtaining a classification result through a classifier, the classification result is used for judging whether the zipper head to be detected has cracks or not, so that the product quality and the production efficiency are further improved, and the sustainable development of enterprises is promoted.
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