Advancing Oyster Phenotype Segmentation with Multi-Network Ensemble and Multi-Scale mechanism

TL;DR

This paper introduces a multi-network ensemble with a multi-scale attention mechanism to improve oyster phenotype segmentation, enabling more accurate and robust identification of oyster components for quality assessment.

Contribution

It presents a novel ensemble and multi-scale approach that enhances segmentation accuracy and robustness in oyster phenotype analysis, addressing scale variability challenges.

Findings

Improved segmentation accuracy across oyster components.

Robust performance on real-world datasets.

Effective handling of multi-scale features.

Abstract

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.