Color Extraction Pipeline for Odonates using Computer Vision

This profile contains the trained models accompanying the scientific paper "Color Extraction Pipeline for Odonates using Computer Vision" by Megan Rajaraman, Fons J. Verbeek, Vincent J. Kalkman and Rita Pucci. The paper is available in arXiv.

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 robustly segment each visible subject (Odonates) into head, thorax, abdomen and wings and then extract a color palette for each body part. This will enable large-scale statistical analysis of ecological correlations (e.g., between coloration and climate change, habitat loss, or geolocation) which are crucial for quantifying and assessing ecosystem biodiversity status.