A UAV Image Dataset for Object Detection with Annotations Generated Using LabelImg and Roboflow

The dataset consists of drone images that were obtained for agricultural field monitoring to detect weeds and crops through computer vision and machine learning approaches. The images were obtained through high-resolution UAVs and annotated using the LabelImg and Roboflow tool. Each image has a corresponding YOLO annotation file that contains bounding box information and class IDs for detected objects. The dataset includes: Original images in .jpg format with a resolution of 4096 × 3072 pixels. Annotation files (.txt) corresponding to each image, following the YOLO format: [class_id x_center y_center width height] (all normalized values). A classes.txt file listing the object categories used in labeling (e.g., Weed, Crop). The dataset is intended for use in machine learning model development, particularly for precision agriculture, weed detection, and plant health monitoring. It can be directly used for training YOLOv7 and other object detection models.本数据集包含用于农田监测的无人机图像，旨在借助计算机视觉（Computer Vision）与机器学习（Machine Learning）方法识别杂草与农作物。图像由高分辨率无人机（Unmanned Aerial Vehicle，UAV）采集，并通过LabelImg与Roboflow工具完成标注。每张图像均配有对应的YOLO（You Only Look Once）标注文件，内含检测目标的边界框信息与类别编号。 本数据集包含以下内容： - 格式为.jpg的原始图像，分辨率为4096×3072像素。 - 与每张图像对应的标注文件（格式为.txt），遵循YOLO格式规范：[class_id x_center y_center width height]（所有数值均为归一化后的值）。 - 一份classes.txt文件，列明标注所用的目标类别（例如杂草、农作物）。 本数据集适用于机器学习模型开发，尤其面向精准农业、杂草检测与植物健康监测场景，可直接用于YOLOv7及其他目标检测模型的训练。

搜集汇总

背景与挑战

背景概述

该数据集包含高分辨率无人机图像和YOLO格式的标注文件，专为农业领域的杂草和作物检测设计，适用于目标检测模型的训练和开发。