Colour Extraction Pipeline for Odonates using Computer Vision

2026-04-20

AI Summary

This study addresses the scarcity of large-scale, annotated morphological and color data for Odonata insects, which has hindered robust statistical analyses linking their traits to ecological factors such as climate. To overcome this limitation, the authors propose a deep neural network–based image segmentation approach that integrates limited labeled data with pseudo-supervised learning to automatically segment key body parts—head, thorax, abdomen, and wings—from open-source images sourced from citizen science platforms. The method further extracts color palettes from each segmented region, enabling precise color quantification. By efficiently leveraging unlabeled data, the framework achieves high-accuracy segmentation and color characterization, thereby establishing a scalable foundation for large-scale ecological color studies and biodiversity analyses in Odonata.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Lexical Semantics and Morphology

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Odonates
• colour extraction
• morphological traits
• ecological correlation
• data annotation

Innovation

• colour extraction
• Odonates segmentation
• deep neural networks
• pseudo-supervised learning
• citizen science images