Abstract
Foundation models (FM) are reshaping computer vision by reducing reliance on task-specific supervised learning and leveraging general visual representations learned at scale. In precision livestock farming, most pipelines remain dominated by supervised learning models that require extensive labeled data, repeated retraining, and farm-specific tuning. This study presents an FM-centered workflow for automated monitoring of group-housed nursery pigs, in which pretrained vision-language FM serve as general visual backbones and farm-specific adaptation is achieved through modular post-processing. Grounding-DINO was first applied to 1,418 annotated images to establish a baseline detection performance. While detection accuracy was high under daytime conditions, performance degraded under night-vision and heavy occlusion, motivating the integration of temporal tracking logic. Building on these detections, short-term video segmentation with Grounded-SAM2 was evaluated on 550 one-minute video clips; after post-processing, over 80% of 4,927 active tracks were fully correct, with most remaining errors arising from inaccurate masks or duplicated labels. To support identity consistency over an extended time, we further developed a long-term tracking pipeline integrating initialization, tracking, matching, mask refinement, re-identification, and post-hoc quality control. This system was evaluated on a continuous 132-minute video and maintained stable identities throughout. On 132 uniformly sampled ground-truth frames, the system achieved a mean region similarity (J) of 0.83, contour accuracy (F) of 0.92, J&F of 0.87, MOTA of 0.99, and MOTP of 90.7%, with no identity switches. Overall, this work demonstrates how FM prior knowledge can be combined with lightweight, task-specific logic to enable scalable, label-efficient, and long-duration monitoring in pig production.
Abstract (translated)
基础模型（FM）正通过减少对特定任务监督学习的依赖并利用大规模学习的通用视觉表征，重塑计算机视觉领域。在精准畜牧业中，大多数流程仍以需要大量标注数据、反复重新训练和农场特定调优的监督学习模型为主。本研究提出了一种以FM为核心的工作流程，用于群养保育猪的自动化监测，其中预训练的视觉-语言FM作为通用视觉主干网络，并通过模块化后处理实现农场特定适应。首先在1418张标注图像上应用Grounding-DINO建立基线检测性能。尽管白天条件下检测准确率较高，但在夜视和严重遮挡场景下性能下降，因此集成了时序追踪逻辑。基于这些检测结果，在550段一分钟视频片段上评估了Grounded-SAM2的短期视频分割效果；经过后处理，4927条活跃追踪轨迹中超过80%完全正确，剩余错误主要源于掩码不准确或标签重复。为实现长期身份一致性，进一步开发了集成初始化、追踪、匹配、掩码精炼、重识别和事后质量控制的长期追踪流程。该系统在连续132分钟视频上进行了评估，全程保持稳定的身份识别。在132个均匀采样的真实帧上，系统达到平均区域相似度（J）0.83、轮廓准确率（F）0.92、J&F值0.87、MOTA指标0.99、MOTP指标90.7%，且无身份切换。总体而言，本研究展示了如何将FM先验知识与轻量级任务特定逻辑相结合，实现猪生产中可扩展、标签高效和长时程监测。
URL
https://arxiv.org/abs/2604.03426
