Yarn Spindle Detection From Trolley Video for Missing Carrier Checks
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Solution Overview
Problem
Inefficient manual inspection of missing yarn spindles during storage and transport leads to delays in packaging processes, requiring labor-intensive and time-consuming manual checks.
Innovation Solution
A detection method and apparatus using video data analysis to determine target areas and identify missing yarn spindles through image processing, generating prompt information for automated detection and reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection is used to detect missing yarn spindles, then the system is simple to implement, but the inspection efficiency is low and labor costs are high
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated optical detection system using cameras and image processing algorithms. The system captures images of the trolley and carriers, automatically identifies missing yarn spindles through image analysis, and generates detection results without human intervention, thereby substituting mechanical manual labor with an automated optical-mechanical system.
Solution Approach 2:
The patent creates visual copies (images) of the physical trolley and carriers using camera systems. These image copies are then processed digitally to detect missing yarn spindles, allowing the system to analyze the physical state of objects through their digital representations without requiring physical manual inspection.
2Loss of time
If manual inspection is performed to identify specific missing yarn spindles, then the detection method is simple, but the time cost is high and delays the packaging process
Solution Approach 1:
The patent performs preliminary detection of missing yarn spindles during the storage period before the trolley departs for packaging. By conducting the inspection in advance using automated image capture and analysis, the system identifies missing spindles proactively, allowing for immediate notification and correction before the packaging process begins, thereby preventing delays in the subsequent packaging workflow.
Solution Approach 2:
The patent replaces time-consuming manual inspection with automated optical detection and image processing. The system rapidly captures images, processes them through algorithms to identify missing yarn spindles, and generates results automatically, significantly reducing the time required compared to manual visual inspection while maintaining accurate identification of specific missing items.
3Ease of operation
If automated image processing is used to detect missing yarn spindles, then the inspection efficiency is improved and labor costs are reduced, but the device complexity increases
Solution Approach 1:
The patent replaces manual mechanical inspection operations with an automated optical detection system that uses cameras, image processing units, and software algorithms. This substitution achieves high automation levels by eliminating the need for human operators to visually inspect each trolley, while the system automatically captures images, processes them, and generates detection results.
Solution Approach 2:
The detection system performs self-service by automatically capturing images of the trolley and carriers, processing the images through embedded algorithms to identify missing yarn spindles, and generating detection results without requiring external manual intervention. The system is self-sufficient in completing the entire inspection workflow autonomously.
Data Source
AI summary
A detection method, an electronic device and a storage medium are provided. The method includes: determining a target area targeted by a target operation in a case where a target body in video data of a target trolley has the target operation, where the target trolley includes two areas, each of the two areas has N carriers for carrying yarn spindles, the target area is one of the two areas, and N is a positive integer; obtaining a plurality of target images capable of covering the target area; determining a target yarn spindle targeted by the target operation based on the plurality of target images; and generating prompt information for the target yarn spindle.