Fire safety consists of a complex pipeline, and it is a very important topic of concern. One of its frontal parts are the smoke detectors, which are supposed to provide an alarm prior to a massive fire appears. As they are often difficult to reach due to high ceilings or problematic locations, an automatic inspection system would be very beneficial as it could allow faster revisions, prevent workers from dangerous work in heights, and make the whole process cheaper. In this study, we present the smoke detector recognition part of the automatic inspection system, which could easily be integrated to the drone system. As part of our research, we compare two popular convolutional-based object detectors YOLOv11 and SSD widely used on embedded devices together with the state-of-the-art transformer-based RT-DETRv2 with the backbones of different sizes. Due to a complicated way of collecting a sufficient amount of data for training in the real-world environment, we also compare several training strategies using the real and semi-synthetic data together with various augmentation methods. To achieve a robust testing, all models were evaluated on two test datasets with an expected and difficult appearance of the smoke detectors including motion blur, small resolution, or not complete objects. The best performing detector is the YOLOv11n, which reaches the average [email protected] score of 0.884. Our code, pretrained models and dataset are publicly available.

翻译：消防安全由复杂流程构成，是备受关注的重要议题。其前端组成部分之一为烟雾探测器，旨在大规模火灾发生前发出警报。由于高天花板或位置特殊导致此类设备常难以触及，自动检测系统将带来显著效益——既可加快检修速度，又能避免工人从事危险高空作业，并降低整体成本。本研究提出自动检测系统中的烟雾探测器识别模块，该模块可便捷集成至无人机系统。作为研究内容，我们比较了两种广泛部署于嵌入式设备的流行卷积目标检测器YOLOv11与SSD，以及采用不同规模骨干网络的最新基于Transformer的RT-DETRv2。考虑到在真实环境中采集足量训练数据的复杂性，我们还对比了多种训练策略，涉及真实数据与半合成数据结合多种数据增强方法。为实现鲁棒性测试，所有模型均基于两个测试数据集进行评估，其中包含预期外观与困难外观的烟雾探测器图像（涵盖运动模糊、低分辨率或目标不完整等情况）。性能最优的检测器YOLOv11n的平均[email protected]得分达0.884。我们的代码、预训练模型及数据集均已公开。