CottonWeedDet12: a 12-class weed dataset of cotton production systems for benchmarking AI models for weed detection

The dataset CottonWeedDet12 consists of 5648 RGB images of 12-class weeds that are common in cotton fields in the southern U.S. states, with a total of 9370 bounding boxes. These images were acquired by either smartphones or hand-held digital cameras, under natural field light condition and throughout June to September of 2021. The images were manually labeled by qualified personnel for weed identification, and the labeling process was done using the VGG Image Annotator (version 2.10). The dataset, at the time of publication, is the largest publicly available multi-class dataset dedicated to weed detection. It expects to facilitate communicate efforts to exploit state-of-the-art deep learning method to push weed recognition to the next level. With the WeedDet12 dataset, a performance benchmark of a suite of YOLO object detectors has been built for weed detection. Detailed documentation of the dataset, model benchmarking and performance results is given in an accompanying journal paper: Dang, F., Chen, D., Lu, Y., Li, Z., 2023. YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture 205, 107655. https://doi.org/10.1016/j.compag.2023.107655 If you use the dataset on a published publication, please cite the dataset or the journal article above.

数据集CottonWeedDet12包含5648张RGB图像，涵盖美国南部各州棉田常见的12类杂草，共计标注有9370个边界框（bounding box）。这些图像由智能手机或手持式数码相机采集，拍摄时间覆盖2021年6月至9月，采集环境为自然田间光照条件。所有图像均由具备资质的人员针对杂草识别任务完成手动标注，标注过程使用VGG图像标注工具（VGG Image Annotator）2.10版本完成。该数据集在发布时，是目前公开可用的规模最大的专用于杂草检测的多类别数据集。本数据集旨在助力相关研究人员利用前沿深度学习方法，推动杂草识别技术实现进一步提升。依托本CottonWeedDet12数据集，研究人员已构建了针对杂草检测任务的一系列YOLO目标检测器的性能基准测试集。本数据集的详细说明、模型基准测试流程及性能测试结果均收录于随附的期刊论文中：Dang, F., Chen, D., Lu, Y., Li, Z., 2023. 《YOLOWeeds：面向棉花生产系统多类别杂草检测的新型YOLO目标检测器基准测试集》，刊载于《计算机与农业电子学（Computers and Electronics in Agriculture）》2023年第205卷，文章编号107655。DOI：10.1016/j.compag.2023.107655。若您在已发表的学术成果中使用本数据集，请引用本数据集或上述期刊论文。

搜集汇总

背景与挑战

背景概述

CottonWeedDet12是一个专注于棉花生产系统中杂草检测的公开数据集，包含5648张RGB图像和9370个边界框标注，覆盖12类常见于美国南部棉花田的杂草。图像采集于2021年自然田间条件下，由专业人员手动标注，是当前最大的多类杂草检测数据集，旨在为AI模型提供基准测试，推动深度学习在杂草识别中的应用。