8头奶牛数据集挑战多动物识别与追踪，助力智慧农业视觉模型发展。
8-Calves Image dataset
- 用YOLOv8与ByteTrack结合半自动标注，生成超53万带时序标签的边界框。
- 严格指标下检测精度仅56.5%-66.4%，暴露细粒度定位难题。
- 小模型在识别与检索间平衡最优，适合资源受限场景部署。
自动化牲畜监测对精准农业至关重要，但现有计算机视觉模型受限于缺乏反映真实群体挑战的数据集。本文提出8-Calves数据集，是一个用于多动物检测、追踪与识别的挑战性基准。该数据集包含一小时的八头荷斯坦弗里西安牛在牛棚中的视频，存在频繁遮挡、运动模糊及多样姿态。通过微调YOLOv8检测器与ByteTrack结合的半自动流水线，并经人工修正，生成超过537,000个带时间身份标签的边界框。我们在28种目标检测器上进行基准测试，发现宽松IoU阈值下性能接近完美（mAP50: 95.2–98.9%），但在更严格指标（mAP50:95）下表现显著分化（56.5–66.4%），凸显细粒度定位困难。23种模型的识别基准揭示：模型规模扩大提升分类准确率，却损害检索能力；较小架构如ConvNextV2 Nano表现最佳（准确率73.35%，Top-1 KNN 50.82%）。以语义学习为重点的预训练（如BEiT）展现出更强迁移能力。追踪方面，领先方法检测精度高（MOTA > 0.92），但身份保持差（IDF1 ≈ 0.27），暴露出遮挡密集场景下的核心挑战。8-Calves数据集通过丰富的时序信息和真实复杂性填补了这一空白，为农业视觉模型发展提供支持。数据集与代码已公开于https://huggingface.co/datasets/tonyFang04/8-calves。
原文摘要 · Abstract (English)
Automated livestock monitoring is crucial for precision farming, but robust computer vision models are hindered by a lack of datasets reflecting real-world group challenges. We introduce the 8-Calves dataset, a challenging benchmark for multi-animal detection, tracking, and identification. It features a one-hour video of eight Holstein Friesian calves in a barn, with frequent occlusions, motion blur, and diverse poses. A semi-automated pipeline using a fine-tuned YOLOv8 detector and ByteTrack, followed by manual correction, provides over 537,000 bounding boxes with temporal identity labels. We benchmark 28 object detectors, showing near-perfect performance on a lenient IoU threshold (mAP50: 95.2-98.9%) but significant divergence on stricter metrics (mAP50:95: 56.5-66.4%), highlighting fine-grained localization challenges. Our identification benchmark across 23 models reveals a trade-off: scaling model size improves classification accuracy but compromises retrieval. Smaller architectures like ConvNextV2 Nano achieve the best balance (73.35% accuracy, 50.82% Top-1 KNN). Pre-training focused on semantic learning (e.g., BEiT) yielded superior transferability. For tracking, leading methods achieve high detection accuracy (MOTA > 0.92) but struggle with identity preservation (IDF1 $\approx$ 0.27), underscoring a key challenge in occlusion-heavy scenarios. The 8-Calves dataset bridges a gap by providing temporal richness and realistic challenges, serving as a resource for advancing agricultural vision models. The dataset and code are available at https://huggingface.co/datasets/tonyFang04/8-calves.
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