Wool fineness determination method and system based on deep learning
Through deep learning-based methods, high-definition cameras and feature extraction algorithms are used to extract the fineness characteristics of wool fibers, and a convolutional neural network model is constructed for prediction, which solves the problems of low accuracy and efficiency of traditional measurement methods, and realizes high-precision and automated wool fineness measurement.
CN119648774AInactive Publication Date: 2025-03-18JILIN AGRICULTURAL UNIV
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Patent Information
- Application Number
- CN202411860780.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-03-18
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Technical Problem
The traditional wool fineness measurement method is complex in operation, time-consuming and measurement accuracy is greatly affected by human factors.
Method used
Using a deep learning-based method, wool fiber images are collected through high-definition cameras, combined with SIFT feature extraction algorithm, Canny edge detection algorithm and grayscale symbiosis matrix, the fineness characteristics of wool fibers are extracted, and a convolutional neural network model is constructed for training and prediction.
Benefits of technology
It realizes high-precision and high degree of automation wool fineness measurement, reduces artificial error, reduces measurement costs, and improves measurement efficiency.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a wool fineness determination method and system based on deep learning, and belongs to the field of wool fineness determination, and the method comprises the following steps: S1, collecting a wool fiber image, and obtaining a data set; s3, obtaining fineness characteristics of wool; s4, constructing a convolutional neural network model, inputting the training set into the convolutional neural network model for training, inputting the verification set into the trained convolutional neural network model, and verifying and evaluating the convolutional neural network model by using cross validation; s5, inputting the test set into the verified convolutional neural network model, predicting the fineness of the wool fibers by using the convolutional neural network model, and outputting a fineness evaluation report; and S6, displaying and storing a result. By adopting the wool fineness determination method and system based on deep learning, wool fineness determination can be efficiently and accurately completed, the advantages of simple operation, non-contact measurement, reliable result and the like are achieved, and the automation level of wool detection is greatly improved.
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