公开了用于玉米田杂草检测的无人机图像数据集，支持多物种识别。
USU-Corn-WeedDB: A UAV RGB Image Dataset for Multi-Species Weed Detection in Forage Corn
- 基于无人机采集真实农田图像，构建多类杂草检测数据集。
- 包含10,539个标注框，红根苋占一半以上，反映真实田间分布。
- 适配轻量模型部署，可直接用于田间智能除草系统开发。
饲用玉米生产中杂草压力可导致最高31.5%的产量损失，而基于无人机影像和深度学习的精准杂草管理（SSWM）受限于缺乏代表真实田间的训练数据。本文发布USU-Corn-WeedDB，一个从犹他州盖斯谷商业饲用玉米田采集的公开无人机RGB图像数据集，旨在支持监督与半监督学习框架下的多类别杂草检测。2025年6月27日，使用Autel EVO II Dual 640T V2无人机在约10米高度获取影像，地面采样距离约为0.48厘米/像素，共获得366张全分辨率图像，切分为8,800个640×640像素图像块。其中800张图像由人工标注，涵盖三类杂草：藜（Chenopodium album）、红根苋（Amaranthus retroflexus）和绿狗尾草（Setaria viridis），共10,539个边界框实例；其余8,000个图像块保留为无标签池，用于半监督实验。数据集保留自然类不平衡特性，其中红根苋占标注实例的53.86%，以贴近真实田间状况。为验证数据集可用性，我们在相同条件下训练了28个目标检测模型，涵盖YOLOv8、YOLOv9、YOLOv10、YOLO11、YOLO26和RT-DETR五大架构家族，未进行超参数调优。测试集[email protected]在0.773至0.840之间，轻量级模型表现优异，适用于边缘部署的无人机系统。该数据集已公开，访问链接：https://doi.org/10.5281/zenodo.20044178。
原文摘要 · Abstract (English)
Weed pressure in forage corn production causes yield losses of up to 31.5%, yet site-specific weed management (SSWM) systems built on UAV imagery and deep learning remain constrained by the scarcity of field-representative training datasets. We present USU-Corn-WeedDB, a publicly available UAV RGB image dataset collected from a commercial forage corn field in Cache Valley, Utah, designed to support multi-class weed detection under both supervised and semi-supervised learning frameworks. RGB imagery was acquired on 27 June 2025 using an Autel EVO II Dual 640T V2 drone at ~10m above ground level, yielding a ground sampling distance of approximately 0.48 cm/pixel. A total of 366 full-resolution images were tiled into 8,800 patches at 640 x 640-pixel resolution. Of these, 800 images were manually annotated for three weed species; common lambsquarters (Chenopodium album), redroot pigweed (Amaranthus retroflexus), and green foxtail (Setaria viridis) comprising 10,539 bounding-box instances, with the remaining 8,000 tiles retained as an unlabeled pool for semi-supervised experiments. This dataset reflects a natural class imbalance where redroot pigweed constitutes 53.86% of annotated instances, which was preserved intentionally to mirror real field conditions. To validate dataset utility, we trained 28 object detection models spanning five architecture families including YOLOv8, YOLOv9, YOLOv10, YOLO11, YOLO26, and RT-DETR under identical conditions without hyperparameter tuning. Test set [email protected] ranged from 0.773 to 0.840, with lightweight models achieving competitive performance relevant to edge-deployed UAV systems. USU-Corn-WeedDB is publicly available at https://doi.org/10.5281/zenodo.20044178.
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