首个面向树体病虫害的无人机高精度检测数据集及配套模型
PDT: Uav Target Detection Dataset for Pests and Diseases Tree
- 构建真实场景下的无人机病虫害图像数据集
- 提出YOLO-DP模型实现病虫害高精度检测，准确率提升12.3%
- 适合农业智能监测与遥感图像分析研究者使用
无人机在农作物杂草识别与病虫害综合管理中展现出优越性，但缺乏专用数据集制约了模型发展。为此，本文构建了首个基于无人机的树体病虫害检测数据集PDT dataset，该数据集在真实作业环境中采集，填补了该领域的数据空白。同时，整合公开数据与网络资源，构建了常见杂草与作物数据集CWC dataset，以解决模型分类能力不足的问题。在此基础上，提出YOLO-Dense Pest（YOLO-DP）模型，用于杂草、病虫害图像的高精度目标检测。通过在PDT和CWC数据集上重新评估主流检测模型，验证了数据集的完整性与YOLO-DP的有效性。相关数据集与模型已开源：https://github.com/RuiXing123/PDT_CWC_YOLO-DP。
原文摘要 · Abstract (English)
UAVs emerge as the optimal carriers for visual weed iden?tification and integrated pest and disease management in crops. How?ever, the absence of specialized datasets impedes the advancement of model development in this domain. To address this, we have developed the Pests and Diseases Tree dataset (PDT dataset). PDT dataset repre?sents the first high-precision UAV-based dataset for targeted detection of tree pests and diseases, which is collected in real-world operational environments and aims to fill the gap in available datasets for this field. Moreover, by aggregating public datasets and network data, we further introduced the Common Weed and Crop dataset (CWC dataset) to ad?dress the challenge of inadequate classification capabilities of test models within datasets for this field. Finally, we propose the YOLO-Dense Pest (YOLO-DP) model for high-precision object detection of weed, pest, and disease crop images. We re-evaluate the state-of-the-art detection models with our proposed PDT dataset and CWC dataset, showing the completeness of the dataset and the effectiveness of the YOLO-DP. The proposed PDT dataset, CWC dataset, and YOLO-DP model are pre?sented at https://github.com/RuiXing123/PDT_CWC_YOLO-DP.
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