دانلود رایگان مقاله سیستم هشدار اضطراری مبتنی بر یادگیری عمیق

Abstract

Emergency alert systems serve as a critical link in the chain of crisis communication, and they are essential to minimize loss during emergencies. Acts of terrorism and violence, chemical spills, amber alerts, nuclear facility problems, weather-related emergencies, flu pandemics, and other emergencies all require those responsible such as government officials, building managers, and university administrators to be able to quickly and reliably distribute emergency information to the public. This paper presents our design of a deep-learning-based emergency warning system. The proposed system is considered suitable for application in existing infrastructure such as closed-circuit television and other monitoring devices. The experimental results show that in most cases, our system immediately detects emergencies such as car accidents and natural disasters.

4. Conclusion

In this paper, we propose an emergency alert system, designed to be suitable for natural disaster detection. The proposed system uses deep-learning technology to detect and analyze disasters. We carried out experimental measurements to assess the performance of our proposed system while increasing the disaster strength and event frequency. The evaluation showed that the average detection time and accuracy for situations involving “fire” demonstrated a higher detection rate than for those involving “car accidents”. These experimental results could be applied in practice by adapting our EAS system to real CCTV or other monitoring devices. Although our computer simulation only generated the above-mentioned two types of emergency events, in real environments, there exist many different emergency situations, not only disasters but also various types of criminal situations. Therefore, in future, we plan to extend our EAS to detect criminal activities and clarify the advantages of the proposed deep-learning-based detection over conventional surveillance systems in terms of accuracy and detection delay.