发表机构
University of Copenhagen; Lund University; University of Wisconsin-Madison（哥本哈根大学; 隆德大学; 威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对蜂群密集遮挡下标记追踪失效问题，提出BeeWhere工作流，融合ArUco检测与YOLO实例分割，量化熊蜂行为指标，提升检测率并捕捉农药暴露引起的空间组织变化。
AI 中文摘要
社会性蜜蜂是重要的传粉者，在全球范围内支持生物多样性和作物授粉，并作为集体行为的重要模式系统，但在密集、遮挡的巢穴环境中，对个体和群体水平行为的可扩展测量仍然困难。现有的监测工作流程使用基准标记（例如ArUco）来保持个体身份，然而基于标记的追踪在标记被遮挡时可能失败，并且关于身体范围、空间背景和未标记个体的信息有限。我们提出了BeeWhere，一个AI辅助的标注和分析工作流程，将ArUco检测与深度学习实例分割相结合，从高分辨率蜂群图像和视频中量化熊蜂行为。以熊蜂（Bombus impatiens）微蜂群为测试案例，我们标注了483帧，包含8,443个蜜蜂实例。我们还标注了花粉球、巢结构和巢室边界，并训练了YOLO实例分割模型用于下游行为分析。实例分割能够基于身体轮廓量化重要的行为指标，包括最近邻距离、与巢结构的接近度、巢内空间占用率以及随时间变化的检测计数。我们将BeeWhere模型应用于基于标记的追踪，在一项评估新烟碱类杀虫剂暴露行为影响的探索性验证研究中。与基于标记的追踪相比，BeeWhere提高了检测率，特别是在蜜蜂部分遮挡或成像条件具有挑战性的情况下，并且还捕捉到了仅使用基于标记的追踪无法捕捉的与处理相关的蜜蜂空间组织变化。这些结果表明，实例分割可以通过在具有挑战性的蜂群条件下恢复行为上有意义的信号来补充基准标记追踪。
英文摘要
Social bees are important pollinators that support biodiversity and crop pollination globally and serve as important model systems for collective behavior, but scalable measurement of individual- and colony-level behavior remains difficult in dense, occluded nest environments. Existing monitoring workflows use fiducial tags (e.g., ArUco) to preserve individual identity, yet tag-based tracking can fail when markers are obscured and provide limited information about body extent, spatial context, and untagged individuals. We present BeeWhere, an AI-assisted annotation and analysis workflow that combines ArUco detections with deep-learnt instance segmentations to quantify bumble bee behavior from high-resolution colony images and videos. Using bumble bee (Bombus impatiens) microcolonies as a test case, we annotate 483 frames containing 8,443 bee instances. We additionally annotate pollen balls, nest structures, and chamber boundaries, and train YOLO instance segmentation models for downstream behavioral analysis. Instance segmentations enable quantification of important behavioral metrics based on body contours, including nearest-neighbor distance, proximity to nest structures, spatial occupancy within the nest, and detection counts over time. We apply the BeeWhere models to tag-based tracking in an exploratory validation study assessing the behavioral impacts of neonicotinoid pesticide exposure. BeeWhere increased detection rates compared to tag-based tracking, particularly when bees were partially obscured or under challenging imaging conditions, and also captured treatment-associated changes in bee spatial organization not captured using tag-based tracking alone. These results suggest that instance segmentation can complement fiducial-marker tracking by recovering behaviorally meaningful signals under challenging colony conditions.
CommentsPreprint. Accepted to ECCV 2026 Computer Vision for Ecology Workshop Proceedings. Proceedings DOI pending
