Fish Eye Image Recognition for Freshness Detection
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Solution Overview
Problem
Existing methods for judging the freshness of cultured fish products, particularly after freezing, are inaccurate due to variations in fish types and changes in eye characteristics, making it difficult to distinguish between fresh, secondary fresh, and stale products using conventional threshold segmentation and edge detection techniques.
Innovation Solution
A method based on eye image recognition that involves obtaining gray scale change sequences, categorizing pixels, calculating average gray scale values, determining fish eye turbidity and plumpness, and using these metrics to assess the freshness of cultured fish products through a combination of exponential and logarithmic functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional threshold segmentation and edge detection methods are used to judge freshness, then the process is simple, but the accuracy is poor due to variations in fish types and eye characteristics
Solution Approach 1:
The patent transforms the detection approach by changing from fixed threshold segmentation to dynamic parameter calculation. It computes multiple parameters including gray scale difference, turbidity index, plumpness index, and curvature radius, which adapt to different fish types and eye characteristics. This allows accurate freshness detection across various fish species without requiring complex manual adjustments to detection parameters.
Solution Approach 2:
The patent replaces conventional mechanical image processing techniques (threshold segmentation and edge detection) with a comprehensive parameter-based assessment system. By substituting simple threshold-based mechanisms with multi-parameter calculation involving gray scale analysis, turbidity measurement, and geometric feature extraction, the system achieves higher accuracy while maintaining computational efficiency.
2Measurement precision
If eye image analysis is used to distinguish freshness grades, then accuracy improves, but the complexity of image processing increases
Solution Approach 1:
The patent segments the eye image into distinct regions including the cornea, iris, and pupil, and analyzes each region separately for specific characteristics. The corneal region is analyzed for turbidity and plumpness, while the iris and pupil provide geometric reference points. This segmentation allows complex freshness assessment to be broken down into manageable analytical steps, improving accuracy without proportionally increasing overall complexity.
Solution Approach 2:
The patent applies different analysis methods to different local regions of the eye image based on their specific characteristics. The corneal area receives detailed turbidity and plumpness analysis, while the iris and pupil provide geometric constraints. This localized quality assessment allows the system to optimize detection for each region's contribution to freshness judgment, achieving high accuracy through targeted analysis rather than uniform processing.
Data Source
AI summary
A method for judging freshness of cultured fish product based on eye image recognition is provided, which relates to the field of image processing and includes the following steps: obtaining eye area and eye center point of each cultured fish product; obtaining a first data category and a second data category of each gray scale change sequence according to each gray scale change sequence of each eye area; calculating a first mean value and a second mean value of each gray scale change sequence; obtaining the fish eye turbidity of each eye area according to the first mean value and the second mean value; obtaining the fish eye plumpness of each eye area according to the first data category and the second data category; and obtaining the freshness of each cultured fish product according to the fish eye turbidity and fish eye plumpness in each eye area.