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影像，旨在通过计算机视觉与机器学习方法识别杂草与农作物。该影像集由高分辨率无人机（UAV）采集，并通过LabelImg与Roboflow工具完成标注。每张影像均配有对应的YOLO格式标注文件，内含检测目标的边界框信息与类别ID。

本数据集包含以下内容：
1. 分辨率为4096×3072像素的.jpg格式原始影像；
2. 与每张影像一一对应的.txt格式标注文件，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）；
3. 包含标注所用目标类别的classes.txt文件（例如杂草（Weed）、农作物（Crop））。

本数据集面向机器学习模型开发场景，尤其适用于精准农业、杂草检测与作物健康监测任务，可直接用于训练YOLOv7及其他目标检测模型。