Abstract The popularity of deep learning has influenced the field of surveillance and human safety. We adopt the advantages of deep learning techniques to recognize potentially harmful objects inside living rooms, offices, and dining rooms during earthquakes. In this study, we propose an educational system to teach earthquake risks using indoor object recognition based on deep learning algorithms. The system is based on the You Look Only Once (YOLO) deployed on our cloud-based server named Earthquake Situation Learning System (ESLS) for the detection of harmful objects associated with risk tags. ESLS is trained on our own indoor images dataset. The user interacts with the ESLS server through video or image files, and the object detection algorithm using YOLO recognizes the indoor objects with associated risk tags. Results show that the service time of ESLS is low enough to serve it to users in 0.8 seconds on average, including processing and communication times. Furthermore, the accuracy of the harmful object detection is 96 % in the general indoor lighting situation. The results show that the proposed ESLS is applicable to real service for teaching the earthquake disaster avoidance.

中文翻译：

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使用深度学习的地震防灾学习系统

摘要 深度学习的流行影响了监控和人类安全领域。我们利用深度学习技术的优势来识别地震期间客厅、办公室和餐厅内的潜在有害物体。在这项研究中，我们提出了一个教育系统，使用基于深度学习算法的室内物体识别来教授地震风险。该系统基于部署在我们名为地震情况学习系统 (ESLS) 的基于云的服务器上的 You Look Only Once (YOLO)，用于检测与风险标签相关的有害物体。ESLS 是在我们自己的室内图像数据集上训练的。用户通过视频或图像文件与 ESLS 服务器进行交互，使用 YOLO 的物体检测算法识别带有相关风险标签的室内物体。结果表明，ESLS 的服务时间足够低，可以在平均 0.8 秒内为用户提供服务，包括处理和通信时间。此外，在一般室内照明情况下，有害物体检测的准确率为 96%。结果表明，所提出的ESLS适用于地震避灾教学的实际服务。