EgoMaize: A First-Person Maize Instance Segmentation Benchmark under Severe Field Occlusion

2026-09-10

AI Summary

研究针对玉米田严重遮挡问题，提出EgoMaize基准，采用证据封闭标注流程处理遮挡区域，以改进第一人称视角下的玉米实例分割。

Abstract

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.

Problem

• First-Person
• Maize Instance Segmentation
• Severe Field Occlusion
• Close-range Images
• Plant-level Traits

Innovation

• First-Person Maize Instance Segmentation
• Severe Field Occlusion
• Evidence-Closed Annotation Workflow