Enterprise AI Analysis
AI-driven classification and precision cutting algorithms using machine vision in a customer-operated fish processing system
This study presents an innovative AI-driven machine vision system for automated fish classification and precision cutting, designed to enhance efficiency and convenience in customer-operated fish processing environments.
Executive Impact & Key Findings
This study introduces an innovative AI-driven machine vision system designed to automate fish classification and precision cutting for customer-operated fish processing. Addressing the challenges of traditional fish cleaning, the system classifies four high-consumption fish species (Silver Carp, Carp, Tigertooth Croaker, Trout) using Artificial Neural Networks (ANN) and Support Vector Machines (SVM) on images captured with a unique backlighted pure blue background for enhanced segmentation. The ANN achieved a remarkable 95.06% accuracy on test data (MSE 2.54x10^-2), while SVM demonstrated even higher performance with 98.75% accuracy (MSE 1.25x10^-2). Following classification, species-specific algorithms determine precise head and belly cutting points, achieving high accuracies: Silver Carp (Head 98.36%, Belly 99.49%), Carp (Head 97.85%, Belly 98.07%), and Trout (Head 96.61%, Belly 97.90%). Tigertooth Croaker cutting points were based on dimension ratios. This intelligent, multi-species processing system significantly enhances efficiency, convenience, and accessibility for consumers, marking a crucial upgrade towards intelligent food processing robots.
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SVM Overall Classification Accuracy (Test Data)
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Automated Processing Speed
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Silver Carp Belly Cut Accuracy
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Highest ANN Test Accuracy
Deep Analysis & Enterprise Applications
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Overall AI Performance
ANN Classification: The Artificial Neural Network (ANN) demonstrated robust performance, with the optimal 6-23-4 structure (6 input features, 23 hidden nodes, 4 output classes). Using the 'Logsig' function in both hidden and output layers with the Levenberg Marquardt (LM) algorithm, the ANN achieved test accuracy of 95.06%, with a corresponding Mean Square Error (MSE) of 2.54 x 10-2.
SVM Classification: The Support Vector Machine (SVM) classifier proved highly effective, achieving an overall classification accuracy of 99.69% on the training data and 98.75% on the test data. The corresponding MSE values were 1.23 x 10-2 for training and 1.25 x 10-2 for testing, indicating excellent generalization. The RBF kernel with a sigma value of 2.25 was identified as the most proper function.
Feature Selection: Both CFS (4 features) and Relief-F (6 features) methods were employed to select the most relevant color, geometrical, and textural features from an initial set of 33, optimizing model performance.
Segmentation & Cutting Precision
Backlight Segmentation: A multipurpose backlighted pure blue background was crucial for distinct fish-background segmentation. The R channel of the RGB color space consistently showed almost zero intensity for the background, ensuring successful isolation of the fish body.
Head Cutting Points: For Silver Carp, the gill arc was the primary head cutting point, achieving 98.36% accuracy. For Carp, similar methods applied, resulting in 97.85% accuracy. For Trout, the pectoral fin's jointing point with the gill was segmented, yielding 96.61% accuracy.
Belly Cutting Points: Silver Carp belly cutting point was segmented based on pixel intensity values, reaching 99.49% accuracy. Carp's belly cutting point also used intensity-based segmentation with 98.07% accuracy. Trout's belly cutting point, using similar fin segmentation techniques, achieved 97.90% accuracy. Tigertooth Croaker cutting points were determined based on fish dimension ratios due to less distinct fin features.
System Architecture & Scalability
Imaging System: A Basler daA1280-54uc USB3 video camera (1.2 MP, 54 fps) with a wide-view fisheye lens was used, ensuring explicit imaging during real-time processing. Images were captured in a controlled chamber with uniform LED illumination.
Processing Workflow: The system follows a sequence: image snapshot, calibration, cropping, R channel extraction, fish segmentation, feature extraction/selection, AI-based classification (ANN/SVM), and finally, species-specific cutting point determination.
Real-time Operation: The intelligent machine processes at least 5 fish per minute (approx. 12 seconds per fish) and can accommodate three fish simultaneously in different processing stages. Computational requirements are handled by a PLC, ensuring real-time performance.
Multi-species Adaptability: Unlike conventional machines specialized for one species, this system classifies different fish species and processes varying dimensions using adaptive algorithms, greatly enhancing its utility and efficiency for diverse aquatic products.
98.75%
SVM Overall Classification Accuracy (Test Data)
Enterprise Process Flow
Taking a snapshot from video camera
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Applying calibration matrix
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Cropping image in predefined dimension
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Extracting R channel
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Segmenting fish from background
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Extracting features & feature selection
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Fish species classification (ANN/SVM)
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Applying species-specific cutting algorithm
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Sending data to fish processing machine
| Feature | ANN (LM-LOG-LOG) | SVM (RBF Kernel) |
|---|---|---|
| Test Accuracy | 95.06% | 98.75% |
| Test MSE | 2.54 x 10-2 | 1.25 x 10-2 |
| Learning Algorithm | Levenberg Marquardt | Sequential Minimal Optimization (Implicit) |
| Optimal Structure | 6-23-4 | RBF (Sigma=2.25) |
| Key Strength | Robustness & Generalization | High Precision for Complex Data |
Intelligent Fish Processing Automation
Problem: Traditional fish processing is laborious, time-consuming, and often specialized for single species, limiting broader adoption. Manual cleaning and cutting processes are inefficient and often lead to waste.
Solution: An AI-driven machine vision system that classifies four common fish species (Silver Carp, Carp, Tigertooth Croaker, Trout) and precisely determines cutting points. It uses a pure blue backlight for robust segmentation and employs ANN and SVM for classification, followed by species-specific cutting algorithms.
Impact: Achieved high classification accuracy (up to 98.75% with SVM) and precise cutting point determination (e.g., Silver Carp belly 99.49%). The system processes 5 fish per minute, supports multiple species, and significantly enhances efficiency, convenience, and accessibility for consumers, minimizing waste.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrate advanced AI solutions into your enterprise, ensuring seamless transition and maximized impact.
Phase 1: Discovery & Strategy
Comprehensive assessment of current operations, identification of AI opportunities, and development of a tailored strategy aligned with your business objectives. Focus on data readiness and infrastructure review.
Phase 2: Pilot & Proof-of-Concept
Deployment of a small-scale AI pilot project to validate the chosen solution's effectiveness, measure initial ROI, and gather feedback for optimization. This phase includes model training and initial integration.
Phase 3: Scaled Implementation
Full-scale integration of the AI solution across relevant departments and workflows, including robust testing, performance monitoring, and user training. Emphasis on change management and system hardening.
Phase 4: Optimization & Continuous Improvement
Ongoing monitoring, performance tuning, and iterative enhancements to the AI models and system. Regular audits and updates to ensure long-term efficiency, adaptability, and competitive advantage.
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