Colour Extraction Pipeline for Odonates using Computer Vision

TL;DR

This paper presents a deep learning pipeline for segmenting and analyzing the coloration of Odonates from open source images, facilitating large-scale ecological studies.

Contribution

It introduces a novel deep neural network-based method to segment Odonate body parts and extract color features using limited annotated data and pseudo supervision.

Findings

Successfully segments Odonates into key body parts.

Extracts color palettes for ecological analysis.

Enables large-scale biodiversity monitoring.

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.