无人机智能巡检多领域数据集

无人机智能巡检多领域数据集是一个面向多场景、多类别视觉检测任务的高质量资源。数据集包含超过5万张标注图像及50万个边界框，涵盖城市管理、公共安全、环境保护、智慧交通、能源巡检和农林监测六大领域。数据采集自真实无人机巡检场景，覆盖多种环境，并兼顾不同时段、天气、光照及地形条件，确保数据的多样性与实际代表性。数据集提供YOLO、YOLOv8 OBB、PASCAL VOC等主流标注格式，包括.txt和.xml文件，并按子数据集分文件夹整理，方便直接用于YOLO系列、Faster R-CNN、MMDetection、Detectron2等框架的训练与评估。
子数据集共九个，具体为：登革热数据集、森林防火数据集、光伏热红外缺陷数据集、工地数据集、车辆车位车牌数据集、河道数据集、交通道路行业数据集、垃圾数据集、井盖数据集。该数据集可广泛应用于公共健康与环境卫生、安全监控与灾害预警、能源与设施运维、交通与城市治理等领域。它不仅适用于目标检测模型的基础训练，还支持细分类、多标签任务及迁移学习，为智慧城市、环境监测、安防巡检、交通管理等行业AI系统的开发与与落地提供完整的数据支撑。

The Multi-Domain Dataset for Intelligent UAV Inspection is a high-quality resource targeting multi-scenario and multi-category visual detection tasks. The dataset contains over 50,000 annotated images and 500,000 bounding boxes, covering six major domains: urban management, public safety, environmental protection, smart transportation, energy inspection, and agricultural and forestry monitoring. The data is collected from real UAV inspection scenarios, covering diverse environments, and takes into account different time periods, weather, lighting and terrain conditions to ensure the diversity and practical representativeness of the dataset. The dataset provides mainstream annotation formats such as YOLO, YOLOv8 OBB and PASCAL VOC, including .txt and .xml files, and is organized into folders by sub-datasets, enabling direct use for training and evaluation of frameworks including the YOLO series, Faster R-CNN, MMDetection, Detectron2 and others. There are a total of nine sub-datasets, specifically: Dengue Fever Dataset, Forest Fire Prevention Dataset, Photovoltaic Thermal Infrared Defect Dataset, Construction Site Dataset, Vehicle, Parking Space and License Plate Dataset, River Channel Dataset, Traffic Road Industry Dataset, Garbage Dataset, and Manhole Cover Dataset. This dataset can be widely applied in fields such as public health and environmental sanitation, security monitoring and disaster early warning, energy and facility operation and maintenance, transportation and urban governance. It is not only suitable for basic training of object detection models, but also supports fine-grained classification, multi-label tasks and transfer learning, providing complete data support for the development and deployment of AI systems in industries such as smart cities, environmental monitoring, security inspection and traffic management.

搜集汇总

背景与挑战

背景概述

无人机智能巡检多领域数据集是一个高质量、多场景的视觉检测资源，包含超过5万张标注图像和50万个边界框，覆盖城市管理、公共安全、环境保护等六大领域，并细分为九个具体子数据集如登革热和森林防火。数据采集自真实无人机巡检场景，涵盖多种环境条件，提供YOLO、PASCAL VOC等主流标注格式，可直接用于YOLO系列、Faster R-CNN等AI框架的训练与评估，适用于智慧城市、环境监测、安防巡检等行业的AI系统开发与落地。