Automatic localization and decoding of honeybee markers using deep convolutional neural networks

TL;DR

This paper introduces a deep learning-based software pipeline for accurately locating and decoding honeybee markers, enabling long-term tracking of individual bees to study their behavior and collective intelligence.

Contribution

It presents a novel deep convolutional neural network approach for honeybee marker localization and decoding, outperforming existing methods and facilitating long-term behavioral studies.

Findings

Outperforms recent similar systems in marker localization and decoding accuracy.

Enables tracking honeybees throughout their lifespan.

Provides an open-source tool to advance honeybee behavior research.

Abstract

The honeybee is a fascinating model animal to investigate how collective behavior emerges from (inter-)actions of thousands of individuals. Bees may acquire unique memories throughout their lives. These experiences affect social interactions even over large time frames. Tracking and identifying all bees in the colony over their lifetimes therefore may likely shed light on the interplay of individual differences and colony behavior. This paper proposes a software pipeline based on two deep convolutional neural networks for the localization and decoding of custom binary markers that honeybees carry from their first to the last day in their life. We show that this approach outperforms similar systems proposed in recent literature. By opening this software for the public, we hope that the resulting datasets will help advancing the understanding of honeybee collective intelligence.