EgoMaize：严重田间遮挡下的第一人称玉米实例分割基准
EgoMaize: A First-Person Maize Instance Segmentation Benchmark under Severe Field Occlusion
AI总结：
针对严重遮挡下第一人称玉米实例分割，提出EgoMaize基准，采用证据闭合标注流程，并分析基线方法，揭示现有架构难以解决精细结构恢复与同类归属的耦合挑战。
AI中文摘要：
近距离第一人称田间图像对于移动式玉米表型分析至关重要，因为许多植株级性状依赖于从俯视角度难以观察的冠层内部结构。然而，出苗后的玉米田构成了一个困难的实例分割场景：茎、叶、雄穗和邻近植株细长、重复且严重遮挡。我们提出了EgoMaize，一个用于第一人称玉米实例分割的紧凑基准，其任务是从具有严重同类重叠的近距离田间图像中预测具有一致归属性的植株掩膜以及植株所属的茎/雄穗线索。现有的仅可见标签可能将一个物理植株分割成不连续的监督信号，而全模态标签可能需要在邻近植株或田间物体后面进行无法验证的补全。因此，EgoMaize采用了一种证据闭合的标注流程来处理被遮挡的玉米区域，并将不可靠的玉米区域分配给忽略类而非背景类。基线结果表明，预训练的基于查询的分组、边界细化和高分辨率裁剪细化有助于任务的不同方面，但没有任何架构能够解决精细结构恢复、同类实例归属和遮挡推理的耦合挑战；遮挡级别分析进一步表明，随着植株可见性的降低，性能会下降。数据集和代码可在https://github.com/JaaaaaaaD/EgoMaize公开获取。
英文摘要：
Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that are difficult to ob serve from overhead views. However, post-seedling maize fields create a difficult in stance segmentation setting: stems, leaves, tassels, and neighboring plants are elon gated, repetitive, and strongly occluded. We introduce EgoMaize, a compact benchmark for first-person maize instance segmentation, where the task is to predict ownership consistent plant masks and plant-owned stem/tassel cues from close-range field images with severe same-class overlap. Existing visible-only labels can fragment one physi cal plant into disconnected supervision, while full-amodal labels may require unverifi able completion behind neighboring plants or field objects. EgoMaize therefore uses an evidence-closed annotation workflow for occluded maize regions and assigns unreli able maize regions to ignore rather than background. Baseline results show that pre trained query-based grouping, boundary refinement, and high-resolution crop refine ment help different aspects of the task, but no architecture solves the coupled chal lenges of fine structure recovery, same-class instance ownership, and occlusion reason ing; occlusion-level analysis further shows that performance decreases as plant visi bility becomes more limited. The dataset and code are publicly available at https: //github.com/JaaaaaaaD/EgoMaize.
