构建首个昆虫多样性多模态数据集，支持批量样本自动分类
A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level
- 融合批量样本影像与个体DNA条形码数据，实现跨尺度标注
- 人工标注1.7万+个体，完成像素级分割与物种标签匹配
- 适用于生态监测与小目标检测研究，推动自动化虫群分析
昆虫包含数百万物种，许多正因环境变化而数量锐减。高通量方法对加速理解昆虫多样性至关重要，其中DNA条形码和高分辨率成像在自动分类中展现出巨大潜力。然而，多数图像方法依赖于单个标本数据，而非大规模生态调查中采集的未分拣批量样本。本文提出混合节肢动物样本分割与识别（MassID45）数据集，用于训练批量昆虫样本的自动分类器。该数据集首次在未分拣样本层级和个体标本层级上同时结合分子与影像数据。人类标注者借助AI辅助工具，在批量图像上完成两项任务：为每个节肢动物个体生成分割掩码，并为超过17,000个标本分配分类标签。将DNA条形码的分类精度与批量图像的精确丰度估计相结合，极大提升了快速、大规模昆虫群落表征的潜力。该数据集推动了微小目标检测与实例分割技术的发展，促进生态学与机器学习研究的创新。
原文摘要 · Abstract (English)
Insects comprise millions of species, many experiencing severe population declines under environmental and habitat changes. High-throughput approaches are crucial for accelerating our understanding of insect diversity, with DNA barcoding and high-resolution imaging showing strong potential for automatic taxonomic classification. However, most image-based approaches rely on individual specimen data, unlike the unsorted bulk samples collected in large-scale ecological surveys. We present the Mixed Arthropod Sample Segmentation and Identification (MassID45) dataset for training automatic classifiers of bulk insect samples. It uniquely combines molecular and imaging data at both the unsorted sample level and the full set of individual specimens. Human annotators, supported by an AI-assisted tool, performed two tasks on bulk images: creating segmentation masks around each individual arthropod and assigning taxonomic labels to over 17 000 specimens. Combining the taxonomic resolution of DNA barcodes with precise abundance estimates of bulk images holds great potential for rapid, large-scale characterization of insect communities. This dataset pushes the boundaries of tiny object detection and instance segmentation, fostering innovation in both ecological and machine learning research.
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