FireSafetyNet: An Image-Based Dataset with Pretrained Weights for Machine Learning-Driven Fire Safety Inspection

This dataset offers a diverse collection of images curated to support the development of computer vision models for detecting and inspecting Fire Safety Equipment (FSE) and related components. Images were collected from a variety of public buildings in Germany, including university buildings, student dormitories, and shopping malls. The dataset consists of self-captured images using mobile cameras, providing a broad range of real-world scenarios for FSE detection. In the journal paper associated with these image datasets, the open-source dataset FireNet (Boehm et al. 2019) was additionally utilized for training. However, to comply with licensing and distribution regulations, images from FireNet have been excluded from this dataset. Interested users can visit the FireNet repository directly to access and download those images if additional data is required. The provided weights (.pt), however, are trained on the provided self-made images and FireNet using YOLOv8. The dataset is organized into six sub-datasets, each corresponding to a specific FSE-related machine learning service: Service 1: FSE Detection - This sub-dataset provides the foundation for FSE inspection, focusing on the detection of primary FSE components like fire blankets, fire extinguishers, manual call points, and smoke detectors. Service 2: FSE Marking Detection - Building on the first service, this sub-dataset includes images and annotations for detecting FSE marking signs. Service 3: Condition Check - Modal - This sub-dataset addresses the inspection of FSE condition in a modal manner, focusing on instances where fire extinguishers might be blocked or otherwise non-compliant. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into 3_1_FSE Condition Check_modal_train_data (containing training images and annotations) and 3_1_FSE Condition Check_modal_val_data_and_weights (containing validation images, annotations and the best weights). Service 4: Condition Check - Amodal - Extending the modal condition check, this sub-dataset involves amodal detection to identify and infer the state of FSE components even when they are partially obscured. This dataset includes semantic segmentation annotations of fire extinguishers. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into 4_1_FSE Condition Check_amodal_train_data (containing training images and annotations) and 4_1_FSE Condition Check_amodal_val_data_and_weights (containing validation images, annotations and the best weights). Service 5: Details Extraction - Inspection Tags - This sub-dataset provides a detailed examination of the inspection tags on fire extinguishers. It includes annotations for extracting semantic information such as the next maintenance date, contributing to a thorough evaluation of FSE maintenance practices. Service 6: Details Extraction - Fire Classes Symbols - The final sub-dataset focuses on identifying fire class symbols on fire extinguishers. This dataset is intended for researchers and practitioners in the field of computer vision, particularly those engaged in building safety and compliance initiatives.本数据集提供了多样化的图像集合，旨在支持用于检测与巡检消防安全设备（Fire Safety Equipment, FSE）及其相关组件的计算机视觉模型开发。图像采集自德国各类公共建筑，包括大学楼宇、学生宿舍及购物中心。本数据集采用移动相机自主拍摄，涵盖了丰富的消防安全设备检测真实应用场景。 在与该数据集相关的期刊论文中，研究团队额外使用了开源数据集FireNet（Boehm等人，2019）开展训练。但为遵守许可协议与分发规范，本数据集未包含FireNet的相关图像。若用户需要额外数据，可直接访问FireNet仓库获取并下载此类图像。不过本次提供的模型权重文件（.pt）是基于本数据集自主采集的图像与FireNet数据集，使用YOLOv8训练得到的。 本数据集共分为6个子数据集，每个子数据集对应一项特定的与消防安全设备相关的机器学习服务： 服务1：消防安全设备检测（FSE Detection）：该子数据集为消防安全设备巡检提供基础支撑，聚焦于核心消防安全设备组件的检测，如灭火毯、灭火器、手动报警按钮及烟雾探测器。 服务2：消防安全设备标识检测（FSE Marking Detection）：基于服务1的任务范畴，该子数据集包含用于检测消防安全设备标识标牌的图像与标注文件。 服务3：状态检查-模态（Condition Check - Modal）：该子数据集以模态方式开展消防安全设备状态巡检，重点关注灭火器被遮挡或存在其他合规性问题的场景。本数据集包含灭火器的语义分割标注。受限于上传要求，该数据集被拆分为3_1_FSE Condition Check_modal_train_data（包含训练图像与标注）及3_1_FSE Condition Check_modal_val_data_and_weights（包含验证图像、标注与最优模型权重）。 服务4：状态检查-非模态（Condition Check - Amodal）：作为模态状态检查的延伸，该子数据集采用非模态检测技术，即使消防安全设备组件被部分遮挡，也可识别并推断其状态。本数据集包含灭火器的语义分割标注。本数据集包含灭火器的语义分割标注。受限于上传要求，该数据集被拆分为4_1_FSE Condition Check_amodal_train_data（包含训练图像与标注）及4_1_FSE Condition Check_amodal_val_data_and_weights（包含验证图像、标注与最优模型权重）。 服务5：细节提取-巡检标签（Details Extraction - Inspection Tags）：该子数据集用于详细提取灭火器上的巡检标签信息，包含用于提取诸如下次维护日期等语义信息的标注，可助力全面评估消防安全设备的运维实践。 服务6：细节提取-火灾类别符号（Details Extraction - Fire Classes Symbols）：最后一个子数据集聚焦于识别灭火器上的火灾类别符号。 本数据集面向计算机视觉领域的研究人员与从业者，尤其是致力于开展安全与合规相关项目的人员。