用多网络集成与多尺度机制提升牡蛎表型分割精度
Advancing Oyster Phenotype Segmentation with Multi-Network Ensemble and Multi-Scale mechanism
- 融合多个模型预测，引入全局-局部注意力机制
- 在真实数据集上实现高鲁棒性实例分割效果
- 适合水产质量评估与机器视觉研究者参考
表型分割对于分析活体生物的视觉特征至关重要，有助于深入理解其特性。以牡蛎为例，肉质评价聚焦于壳、肉、生殖腺和肌肉等组分。传统人工检测耗时且主观，促使采用机器视觉技术实现高效客观评估。本文探索机器视觉在分割牡蛎组分中的能力，提出一种结合多网络集成与全局-局部层级注意力机制的方法。该方法整合不同模型的预测结果，应对组分尺度差异带来的挑战，实现跨组分的稳健实例分割。最后，基于多个真实世界数据集对所提方法进行全面评估，验证其在提升牡蛎表型分割效能与鲁棒性方面的有效性。
原文摘要 · Abstract (English)
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.
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