Automated Shrimp Peeling System Using Vision-Based Classification
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Solution Overview
Problem
Manual adjustment of shrimp-peeling equipment is labor-intensive and requires experienced monitoring to optimize throughput and quality, which can be inconsistent due to factors like shrimp species, size, and equipment wear.
Innovation Solution
An automated shrimp-processing system that uses a conveyor and an off-line QC station with a vision system to classify shrimps based on digital images, determining the number of tail segments and estimating full weight to adjust peeling equipment parameters for improved quality and yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustment of shrimp-peeling equipment is used, then experienced operators can optimize throughput and quality, but it is labor-intensive and inconsistent
Solution Approach 1:
The patent replaces manual visual inspection and adjustment with an automated vision system using cameras and image processing algorithms. The system captures images of peeled shrimp, automatically detects quality attributes such as shell fragments and segmentation completeness, and provides feedback for equipment adjustment without requiring human operators to continuously monitor and adjust settings
Solution Approach 2:
The vision system enables the peeling equipment to self-regulate by providing automated quality assessment and feedback. The system continuously monitors peeling quality and automatically adjusts equipment parameters based on detected defects, allowing the system to maintain optimal performance without constant human intervention
2Manufacturing precision
If manual monitoring and adjustment is performed, then peeling quality can be optimized, but it requires diligent monitoring and experience
Solution Approach 1:
The patent replaces complex human judgment and experience-based adjustment with an automated vision system that uses computer algorithms to objectively assess peeling quality. The system processes images through algorithms that detect segmentation completeness, shell fragments, and other quality attributes, providing consistent and repeatable measurements without requiring operator expertise
Solution Approach 2:
The vision system implements closed-loop feedback by continuously monitoring peeling quality and automatically adjusting equipment parameters based on detected defects. The system provides real-time feedback on segmentation quality, shell fragment presence, and other metrics, enabling automatic correction of peeling parameters to maintain optimal quality levels
3Measurement precision
If automated vision classification is implemented, then shrimp classification accuracy is improved, but equipment complexity increases
Solution Approach 1:
The patent replaces manual classification with an automated vision system that uses digital imaging and image processing algorithms. The system captures images of shrimp, automatically detects tail segments and estimates weight through computer vision techniques, and classifies shrimp into size categories without requiring physical measurement or human judgment
Solution Approach 2:
The vision system creates digital copies (images) of the shrimp and performs classification on these copies rather than requiring physical manipulation or measurement of the actual shrimp. This allows for non-contact, high-speed classification with consistent precision while reducing the complexity of physical measurement devices
Data Source
AI summary
Methods and systems using a vision system to process shrimp. The in-line or off-line vision system captures images of samples of shrimps. The processor produces a digital image of the shrimps in the samples. Shrimps exiting a peeler are imaged to determine the number of tail segments in or the percentage of full weight of each. The shrimps are classified by the number of intact segments, and quality, yield, and throughput computed from the classification results. The processor can control operational settings of the peeler based on the classification results. In a larger system including other shrimp-processing equipment besides the peeler, other points along the processing path can be imaged by camera or sensed by other sensors to determine processing quality and to make automatic operational adjustments to the equipment.