用计算机视觉自动提取蜻蜓身体各部分颜色，助力生态大尺度研究
Colour Extraction Pipeline for Odonates using Computer Vision
- 基于深度网络构建蜻蜓体部分割与颜色提取流水线
- 利用开源图像和伪监督数据实现小样本高效训练
- 适合生态学、生物多样性监测及公民科学项目使用
昆虫形态特征与气候的关系已有生理学研究记录，但受限于数据分析耗时，相关研究进展缓慢。开源数据集普遍缺乏物种形态特征标注，需专门开展标注工作，且多为局部、成本高昂。本文提出一种基于深度神经网络的蜻蜓（包括蜉蝣）体部识别与分割流水线，旨在提取各部位颜色信息。该方法在有限标注数据上训练，并通过公民科学平台获取的开源图像进行伪监督优化。实验表明，该方法可将每个可见个体精准分割为头部、胸部、腹部和翅膀，并为每部分提取颜色色板。这为大规模统计分析生态关联（如颜色与气候变化、栖息地丧失或地理位置的关系）提供了可能，对量化评估生态系统生物多样性状态具有重要意义。
原文摘要 · Abstract (English)
The correlation between insect morphological traits and climate has been documented in physiological studies, but such studies remain limited by the time-consuming nature of the data analysis. In particular, the open source datasets often lack annotations of species' morphological traits, making dedicated annotations campaigns necessary; these efforts are typically local in scale and costly. In this paper, we propose a pipeline to identify and segment body parts of Odonates (dragonflies and damselflies) using deep neural networks, with the ultimate goal of extracting body parts' colouration. The pipeline is trained on a limited annotated dataset and refined with pseudo supervised data. We show that, by using open source images from citizen science platforms, our approach can segment each visible subject (Odonates) into head, thorax, abdomen, and wings and then extract a colour palette for each body part. This will enable large-scale statistical analysis of ecological correlations (e.g., between colouration and climate change, habitat loss, or geolocation) which are crucial for quantifying and assessing ecosystem biodiversity status.
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