Yarn Spindle Image Counting for Trolley Exit Detection
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Solution Overview
Problem
The manual sampling detection method for yarn spindles during transportation has inefficiencies and a high missed detection rate, affecting subsequent packaging processes.
Innovation Solution
A detection method and apparatus using image acquisition and machine learning models to count yarn spindles entering and leaving a target area, generating detection information based on the quantities in entering and leaving images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual sampling detection method is used, then detection process is simple to implement, but detection efficiency is low and missed detection rate is high
Solution Approach 1:
The patent replaces the manual mechanical detection system with an automated image-based detection system. Image acquisition devices capture images of yarn spindles on the trolley, and image processing algorithms automatically count and analyze the spindles, substituting human manual sampling with an automated visual inspection system that achieves 100% detection coverage without manual intervention
Solution Approach 2:
The patent creates visual copies (images) of the yarn spindles using image acquisition devices. These images serve as digital replicas that can be processed, analyzed, and counted without physically handling or moving the actual spindles, enabling non-contact detection and preserving the original arrangement while achieving accurate counting
2Measurement precision
If manual sampling detection is performed, then detection cost is low, but detection accuracy is poor with high missed detection rate
Solution Approach 1:
The patent implements continuous detection by capturing images of all yarn spindles on the trolley without interruption or sampling. The image acquisition system continuously monitors and records the spindle arrangement, ensuring that every spindle is detected and counted, eliminating the gaps and missed detections inherent in manual sampling methods
Solution Approach 2:
The patent introduces image processing algorithms and detection models as intermediaries between the physical yarn spindles and the detection result. These computational intermediaries automatically analyze the captured images, identify spindle boundaries, count spindles, and generate detection reports, eliminating the need for manual counting and significantly improving both accuracy and efficiency
3Extent of automation
If automated image-based detection is implemented, then detection efficiency is improved and accuracy is enhanced, but system complexity increases
Solution Approach 1:
The patent designs a multi-functional integrated detection system that combines image acquisition, image processing, spindle identification, counting, and anomaly detection capabilities in a single platform. The system can handle multiple detection tasks (counting, positioning, quality inspection) using the same hardware and software infrastructure, reducing the need for separate specialized devices and minimizing overall system complexity
Data Source
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AI summary
Provided is a detection method and apparatus and a storage medium. The method includes: obtaining (S101) an entering image and a leaving image when detecting that a trolley leaves an area; where the entering image is obtained by performing image acquisition on the trolley after the trolley enters the area, and the leaving image is obtained by performing image acquisition on the trolley after the trolley leaves the area; inputting (S 102) the entering image into a target detection model to obtain a first quantity of yarn spindles contained in the entering image, and inputting the leaving image into the target detection model to obtain a second quantity of yarn spindles contained in the leaving image; and generating (S103) detection information for the trolley based on the first quantity of yarn spindles contained in the entering image and the second quantity of yarn spindles contained in the leaving image.