COMPUTER VISION FOR SPLENDID SQUID SIZE AND SPECIES CLASSIFICATION
Abstract
Thai seafood industry relies heavily on labor intensive work, especially in a classification process. Classifying size and species of splendid squids can be exhausted and prone to errors. This study investigated approaches to automate size sorting of splendid squid and differentiating its species such as L.duvauceli and L.chinensis. Parameters extracted from squids’ images for example, area, width, length and constructive geometries were tested for their significance. As functions of these parameters, classifiers for grading size were developed based on regression analysis and neural network models; a discrimination analysis was employed in species sorting. Neural network at the accuracy of 92.67% was found to marginally outperform the regression model (88% accuracy) in size prediction; however, non-linear regression was recommended in practice due to its simplicity to apply. For species differentiation, the discrimination equation was as accurate as the crisp divider (width over length ratio) at approximately 90%. These computerized approaches in size and species classification were proven to be superior to manual practice; they can overcome limitations of work performance due to individual capability and ergonomic factors.
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References
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