# Colour Extraction Pipeline for Odonates using Computer Vision

arXiv: https://arxiv.org/abs/2604.18725 · HTML: https://mlsanity.com/paper/2604.18725/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM](https://mlsanity.com/paper/2404.04996/) (2024)
- [Insect Identification in the Wild: The AMI Dataset](https://mlsanity.com/paper/2406.12452/) (2024)
- [Learning Part Segmentation from Synthetic Animals](https://mlsanity.com/paper/2311.18661/) (2023)
- [A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level](https://mlsanity.com/paper/2507.06972/) (2025)
- [Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings](https://mlsanity.com/paper/2212.00423/) (2022)

## Abstract (arXiv)

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.