DINOv3视觉表示在蓝莓感知中的应用：面向机器人采摘
DINOv3 Visual Representations for Blueberry Perception Toward Robotic Harvesting
AI总结：
DINOv3作为语义主干网络，在蓝莓机器人采摘中用于果实和损伤分割及检测，但检测受限于目标尺度变化和空间聚合建模的挑战。
摘要：
基于大规模自监督学习训练的视觉基础模型在视觉感知方面表现出强大的泛化能力；然而，它们在农业环境中的实际作用和性能限制仍不够清楚。本研究评估DINOv3作为冻结主干网络，在蓝莓机器人采摘相关的视觉任务中的应用，包括果实和损伤分割，以及果实和簇检测。在统一的协议下使用轻量级解码器，分割任务始终受益于稳定的块级表示，并且随着主干网络大小的增加而扩展。相比之下，检测任务受到目标尺度变化、块离散化以及定位兼容性的影响。簇检测的失败突显了在由空间聚合定义的关系目标建模方面的局限性。总体而言，DINOv3最好不被视为端到端任务模型，而应视为一个语义主干网络，其有效性取决于下游空间建模与果实尺度和聚合结构的一致性，为蓝莓机器人采摘提供指导。代码和数据集将在接受后提供。
英文摘要：
Vision Foundation Models trained via large-scale self-supervised learning have demonstrated strong generalization in visual perception; however, their practical role and performance limits in agricultural settings remain insufficiently understood. This work evaluates DINOv3 as a frozen backbone for blueberry robotic harvesting-related visual tasks, including fruit and bruise segmentation, as well as fruit and cluster detection. Under a unified protocol with lightweight decoders, segmentation benefits consistently from stable patch-level representations and scales with backbone size. In contrast, detection is constrained by target scale variation, patch discretization, and localization compatibility. The failure of cluster detection highlights limitations in modeling relational targets defined by spatial aggregation. Overall, DINOv3 is best viewed not as an end-to-end task model, but as a semantic backbone whose effectiveness depends on downstream spatial modeling aligned with fruit-scale and aggregation structures, providing guidance for blueberry robotic harvesting. Code and dataset will be available upon acceptance.
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