In this paper, we show that paint markings are a feasible approach to automatize the analysis of behavioral assays involving honey bees in the field where marking has to be as lightweight as possible. We contribute a novel dataset for bees re-identification with paint-markings with 4392 images and 27 identities. Contrastive learning with a ResNet backbone and triplet loss led to identity representation features with almost perfect recognition in closed setting where identities are known in advance. Diverse experiments evaluate the capability to generalize to separate IDs, and show the impact of using different body parts for identification, such as using the unmarked abdomen only. In addition, we show the potential to fully automate the visit detection and provide preliminary results of compute time for future real-time deployment in the field on an edge device.

翻译：本文证明，在需要最小化标记影响的野外场景中，油漆标记是实现蜜蜂行为试验自动分析的可行方法。我们构建了一个包含4392张图像、27个身份标签的新型蜜蜂油漆标记再识别数据集。基于ResNet主干网络和三元组损失的对比学习方法，在身份预知的封闭场景下，实现了近乎完美的身份表征特征识别。通过多样化实验评估了模型对独立身份标识的泛化能力，并揭示了使用不同身体部位（如仅使用未标记腹部）进行识别的效果差异。此外，我们展示了实现完全自动化访花检测的潜力，并给出了未来在边缘设备上实时部署的计算时间初步结果。