发表机构
Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过站点分离评估证明，消除空间数据泄漏后，近红外相机在农业可通行性任务中并无优势，其表观性能提升是站点特定伪影，添加前视NIR相机仍缺乏证据支持。
AI 中文摘要
一旦消除空间数据泄漏，近红外（NIR）成像在白天农业可通行性任务中并不持续优于标准彩色相机。先前表明NIR优势的基准测试采用了序列级划分，使得空间自相关的图像进入测试集，人为夸大了NIR性能，尤其是在困难的田埂分割任务上。当使用AI Hub自动驾驶语料库在严格保留的录制站点上进行评估时，四种测试配置（彩色、NIR、亮度控制及其融合）在五类可通行性类别中均未能可靠地超越标准彩色。此外，在单个保留站点观察到的表观性能提升，在轮换保留站点时完全无法复现，表明所观察到的优势是站点特定的伪影而非可泛化的改进。校正空间泄漏完全消除了NIR的表观领先优势，而非均匀地降低所有传感器类型的性能。因此，在日间农业车辆传感器套件中添加前视NIR相机仍未被证实有效，而受位置泄漏影响的评估可能扭曲传感器排名并误导采购决策。
英文摘要
Near-infrared (NIR) imaging does not consistently outperform standard color cameras for daytime agricultural traversability once spatial data leakage is eliminated. Prior benchmarks suggesting an NIR advantage used sequence-level splits that permitted spatially autocorrelated imagery into test sets, artificially inflating NIR performance, especially on difficult paddy-boundary segmentation. When evaluated across strictly held-out recording sites using the AI Hub autonomous driving corpus, none of the four tested configurations (color, NIR, a luminance control, or their fusion) reliably surpasses standard color across any of the five traversability classes. Furthermore, apparent performance gains observed at a single held-out site completely fail to replicate when the held-out site is rotated, demonstrating that the observed benefits were site-specific artifacts rather than generalizable improvements. Correcting for spatial leakage erases the apparent lead of NIR entirely rather than uniformly degrading performance across all sensor types. Consequently, adding a forward-facing NIR camera to a daylight agricultural vehicle sensor suite remains unproven, and evaluations compromised by location leakage risk distorting sensor rankings and misleading procurement decisions.
