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.

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。