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.

使用蜜蜂行为分析中的油漆标记实现自动化的研究表明，对于轻量级标记，油漆标记是可行的；我们提供了一个由 4392 个图像和 27 个标识组成的蜜蜂重新识别的新型数据集，使用 ResNet 主干和三元组损失的对比学习导致几乎完美的身份识别特征；多样化实验评估了推广到单独标识的能力，并展示了仅使用未标记的腹部进行识别的影响；此外，我们展示了完全自动化的访问检测的潜力，并提供了未来在边缘设备上实时部署的计算时间的初步结果。