Advancing Oyster Phenotype Segmentation with Multi-Network Ensemble and Multi-Scale mechanism
This work addresses the problem of time-consuming and subjective manual inspection in oyster meat quality assessment for aquaculture or food industry applications, representing an incremental improvement in machine vision methods.
The paper tackles oyster phenotype segmentation for meat quality assessment by developing a multi-network ensemble approach with a global-local hierarchical attention mechanism, achieving robust instance segmentation across components as evaluated on real-world datasets.
Phenotype segmentation is pivotal in analysing visual features of living organisms, enhancing our understanding of their characteristics. In the context of oysters, meat quality assessment is paramount, focusing on shell, meat, gonad, and muscle components. Traditional manual inspection methods are time-consuming and subjective, prompting the adoption of machine vision technology for efficient and objective evaluation. We explore machine vision's capacity for segmenting oyster components, leading to the development of a multi-network ensemble approach with a global-local hierarchical attention mechanism. This approach integrates predictions from diverse models and addresses challenges posed by varying scales, ensuring robust instance segmentation across components. Finally, we provide a comprehensive evaluation of the proposed method's performance using different real-world datasets, highlighting its efficacy and robustness in enhancing oyster phenotype segmentation.