Yarn Spindle Neural Detection for Automated Defect Grading
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Solution Overview
Problem
The chemical fiber industry faces inefficiencies in defect detection and level evaluation of yarn spindles due to reliance on manual experience, which affects production and management efficiency.
Innovation Solution
An automated method and apparatus for processing yarn spindle data, utilizing a neural network model to perform defect detection and level evaluation based on detection results, enabling automatic adjustment of spindle levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual defect detection and level evaluation methods are used, then workers can perform defect detection on individual yarn spindles, but the detection method is highly dependent on manual experience and is inefficient
Solution Approach 1:
The patent replaces the manual mechanical detection system with an automated image processing and neural network-based detection system. The system uses image acquisition devices to capture yarn spindle images and employs neural networks to automatically identify defects, eliminating dependence on manual experience while significantly improving detection efficiency and consistency.
2Productivity
If automated defect detection is implemented, then detection efficiency is improved, but the system complexity increases
Solution Approach 1:
The patent implements a multi-functional integrated system that combines image acquisition, neural network-based defect detection, and automatic level evaluation in a single platform. This universal system handles multiple tasks (defect identification, quality assessment, and grading) simultaneously, improving processing speed while managing system complexity through functional integration rather than separate independent systems.
Data Source
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AI summary
The present disclosure provides a method and an apparatus for processing yarn spindle data, an electronic device and a storage medium. The present disclosure relates to the field of data processing technology, and in particular to a method and an apparatus for processing yarn spindle data, a device and a storage medium. The method includes: after determining that a yarn spindle transported enters a detection area, performing (S101) defect detection on the yarn spindle located in the detection area to obtain a target detection result of the yarn spindle; where the target detection result is used to characterize a defect degree of the yarn spindle; and after determining (S102) that the target detection result meets a preset defect requirement, obtaining a target level of the yarn spindle based on the target detection result of the yarn spindle and a preset level of the yarn spindle.