Computer Vision Sample Bottle Sorting for Automated Sampling
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Solution Overview
Problem
Existing detection devices require manual rearrangement and resetting of sample bottles to perform different tests, consuming labor and time, as they lack automated methods to identify and sort samples based on predefined placement and sampling sequences.
Innovation Solution
A control method and device using computer vision to identify sample bottle labels, group them by type, and control actuators to collect samples sequentially based on predefined testing manners, eliminating the need for manual rearrangement and reducing labor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual rearrangement and resetting of sample bottles is performed for each sample type, then the detection device can perform different tests on different samples, but labor and time are consumed
Solution Approach 1:
The system uses computer vision to automatically identify sample bottle labels and sorting positions, then autonomously rearranges samples and controls the detector bar without manual intervention. The detection device serves itself by automatically organizing samples based on visual recognition, eliminating the need for manual rearrangement while maintaining the ability to perform different tests on different samples
Solution Approach 2:
The patent replaces manual mechanical operations with an automated system combining computer vision (optical recognition) and controlled actuation. The camera circuit captures images of sample bottles, the processor identifies labels and positions, and the actuator automatically moves the detector bar, substituting manual labor with an automated electromechanical system
2Adaptability or versatility
If manual rearrangement of sample bottles is performed, then different testing manners can be applied to different samples, but labor is required
Solution Approach 1:
The system automatically identifies sample bottles through computer vision, determines their sorting positions based on label recognition, and autonomously rearranges them without requiring manual intervention. The detection device performs self-organization of samples, making operation simple while maintaining versatility for different testing manners
Solution Approach 2:
The patent introduces a camera circuit as an intermediary to visually identify sample bottles and their labels, and a processor as an intermediary to interpret visual information and control the actuator. This intermediary system bridges the gap between diverse sample types and automated processing, enabling the system to handle different samples without direct manual intervention
3Extent of automation
If sample bottles are placed in predefined positions, then automated detection can be performed, but flexibility in sample placement is reduced
Solution Approach 1:
The system dynamically determines the correct placement position for each sample bottle based on real-time visual recognition of its label and type. Instead of requiring static predefined positions, the system adapts the placement location according to the specific sample characteristics identified by the camera circuit and processor, maintaining flexibility while achieving automation
Solution Approach 2:
The patent changes the parameter of position determination from fixed predefined locations to dynamic positions based on visual recognition data. The processor identifies label information and calculates appropriate sorting positions, allowing sample bottles to be placed flexibly and then automatically repositioned to the correct location for testing
Data Source
AI summary
A control method based on computer vision is disclosed, the method includes: photographing multiple sample bottles on a tray in a top-view manner to generate a top-view image; performing an object detection process on the top-view image to identify a type of a label on a cap of each sample bottle and a placement position of each sample bottle in a chamber; dividing the multiple sample bottles into multiple groups based on the respective types of the multiple labels, and sorting the multiple groups to generate a sampling order; controlling an actuator to drive a detector bar on a lifting arm in the actuator to sequentially collect samples in the multiple groups based on the sampling order and the multiple placement positions.