AI Vision Inspection of Fruit Vesicles for Color and Foreign Matter
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Solution Overview
Problem
Existing methods for detecting fruit vesicle abnormal color and foreign matter are inadequate, particularly when fruits are unpacked, and rely heavily on human visual inspection, failing to identify subtle abnormalities that can pose health risks and affect product quality and consumer satisfaction.
Innovation Solution
An automated detection system using AI vision with modules for camera adjustment, raw material barrel position detection, abnormal color and foreign matter analysis, and control, employing high-definition cameras and microcomputers to analyze fruit vesicles and output detection results for continued conveyance, warning, or scrapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human visual inspection is used for detecting abnormal color and foreign matter in fruit vesicles, then the detection method is simple and low-cost, but the detection precision and ability to identify subtle abnormalities are insufficient
Solution Approach 1:
The patent replaces human visual inspection with an automated vision detection system comprising cameras, image processing units, and analysis algorithms. This substitution enables objective, precise, and consistent detection of abnormal colors and foreign matters without relying on human subjective judgment, thereby significantly improving measurement precision while maintaining operational simplicity through automation.
Solution Approach 2:
The patent uses optical copying through cameras to capture images of fruit vesicles, creating digital replicas that can be analyzed computationally. This copying approach allows multiple analyses of the same object without physical contact, enables precise measurement of subtle abnormalities, and facilitates automated decision-making based on image data rather than human observation.
2Productivity
If automated vision detection system is implemented, then the detection precision and productivity are improved, but the device complexity increases
Solution Approach 1:
The patent implements continuous detection capabilities where the vision system operates without interruption, continuously capturing images and analyzing fruit vesicles as they pass through the inspection area. This continuous operation eliminates the need for manual inspection intervals, significantly improving productivity and detection efficiency while the automated nature of the system handles the complexity of processing multiple images and data points.
Solution Approach 2:
The detection system performs self-analysis through automated image processing algorithms that independently identify and classify abnormal colors and foreign matters. The system self-regulates by automatically adjusting detection parameters, processing images, and generating results without requiring external intervention, thereby improving productivity while managing system complexity through autonomous operation.
3Reliability
If multiple detection modules are added to improve detection comprehensiveness, then the detection capability is enhanced, but the device complexity and cost increase
Solution Approach 1:
The patent designs a multi-functional detection system where a single integrated platform performs multiple detection tasks including abnormal color identification, foreign matter detection, and quality assessment. The vision system uses multiple algorithms and analysis methods within one unified structure, achieving comprehensive detection reliability without proportionally increasing device complexity or cost compared to multiple separate systems.
Solution Approach 2:
The patent combines multiple detection functions into an integrated system where cameras, image processing units, and analysis modules work together as a unified whole. By merging color analysis, foreign matter detection, and quality assessment into one coordinated system, the patent achieves enhanced detection reliability while avoiding the complexity and cost of implementing separate independent detection systems for each function.
Data Source
AI summary
An automated detection system based on AI vision for fruit vesicle abnormal color and foreign matter proposes to use a high-definition camera in combination with a microcomputer to collect high-frequency and high-definition photographs of surface of fruit vesicle raw material barrel when it's unpacked, analyze information, and then output detection results of each fruit vesicle raw material barrel, and accordingly realize demand for continued conveyance, early warning, elimination, and scrapping of the fruit vesicle raw material barrel, and replace the human eye in detecting smaller abnormal-color spots, which reduces the risk of foreign matter and abnormal color in the opening package of vesicle, and avoids food poisoning, allergic reaction or other health problems that may be caused by pests, chemical residues, mechanical damages and other reasons after being ingested into the human body, and protects health of the human body and food safety of consumers.