火灾检测数据集

火灾检测数据集，火灾检测数据集是一个专门用于构建应变决策模型而采集的数据集，总容量为135MB，以JPG格式存储。数据集中的图像从多个公开的网络资源，如火灾监测网站、新闻媒体发布的火灾现场图片、以及一些公开的图像数据库中收集而来。图像涵盖了不同的环境条件（如白天、夜晚、室内、室外、不同天气状况等）以及不同的火焰规模和类型。该数据集包含两类图片：有火和无火的场景图像，每个图像均赋予一个唯一的图像编号，并标注其类别（有火1或无火0）。在数据预处理阶段，首先对图像进行质量检查，去除模糊、过暗或过亮等不符合要求的图像，确保数据的可用性。对图像进行尺寸统一和格式转换，使其符合多智能体强化学习算法的输入要求。对图像进行数据增强处理，如旋转、缩放、裁剪等，以增加数据集的多样性和丰富性，提高模型的泛化能力。通过处理为火灾检测算法的开发和验证提供高质量的视觉数据，以支持快速准确的火灾识别和分类。Fire Detection Dataset: This dataset is collected specifically for building emergency decision-making models, with a total capacity of 135MB and stored in JPG format. The images in the dataset are gathered from multiple public web resources, including fire monitoring websites, fire scene images released by news media, and some public image databases. The images cover various environmental conditions (e.g., daytime, nighttime, indoor, outdoor, different weather conditions, etc.) as well as different flame scales and types. This dataset contains two categories of images: scenes with fire and scenes without fire. Each image is assigned a unique image ID and labeled with its category (1 for fire, 0 for no fire). In the data preprocessing stage, quality checks are first performed on the images to remove unqualified ones such as blurry, overly dark or overly bright images, ensuring data availability. Then, uniform resizing and format conversion are conducted on the images to meet the input requirements of multi-agent reinforcement learning algorithms. Additionally, data augmentation operations like rotation, scaling and cropping are applied to increase the diversity and richness of the dataset and improve the model's generalization ability. This dataset provides high-quality visual data for the development and validation of fire detection algorithms, supporting rapid and accurate fire recognition and classification.

搜集汇总

背景与挑战

背景概述

该数据集是一个专门用于火灾检测算法开发和验证的视觉数据集，总容量为135MB，包含有火和无火两类场景图像，涵盖不同环境条件。图像经过质量检查、尺寸统一和数据增强等预处理，以支持快速准确的火灾识别和分类。