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.棉花杂草检测数据集12（CottonWeedDet12）包含5648张RGB图像，涵盖美国南部棉田常见的12类杂草，共计标注有9370个边界框（bounding box）。该数据集的图像均采集于2021年6月至9月，拍摄场景为自然田间光照环境，采集设备涵盖智能手机与手持式数码相机。所有图像均由具备资质的专业人员针对杂草识别任务完成手动标注，标注工作借助VGG图像标注工具（VGG Image Annotator，版本2.10）实现。 该数据集在发布之际，是当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，旨在助力前沿深度学习方法的研发与应用，推动杂草识别技术迈上新台阶。依托CottonWeedDet12数据集，研究人员已构建了一套针对杂草检测任务的YOLO目标检测器性能基准。关于该数据集的详细说明、模型基准测试结果与性能表现数据，可参阅配套发表的期刊论文：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, 2023, 205: 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。