A New Efficient-Attention Based Disaster Classification for Emergency Monitoring - Aydin, I.; Karabulut, Y. (Institute of Electrical and Electronics Engineers Inc., 2024)
Natural disasters such as earthquakes, floods and storms have increased today due to climate change. Since infrastructure problems often occur in places where natural disasters occur, it is vital to reach the disaster area as quickly as possible. In such disaster situations, unmanned aerial vehicles can be used, which have significant advantages with their flexibility, low cost and mobility features. In this study, a low-weight deep learning approach that can work on UAV is presented to detect the disaster situation and perform emergency response. The proposed approach is based on the EfficentNetv2 model, which has offered significant advantages in recent years, especially in terms of speed and accuracy. By adding an attention mechanism to this model, its performance has been increased. Experimental results showed that the proposed approach achieved around 6% accuracy compared to the base EfficentNetv2S model. © 2024 IEEE.
2024 28th International Conference on Information Technology, IT 2024 -- 21 February 2024 through 24 February 2024 -- Zabljak -- 198446
Keywords: Antennas; Climate change; Deep learning; Aerial vehicle; Attention mechanisms; Disaster areas; Disaster situations; Emergency monitoring; Emergency response; Learning approach; Low mobility; Low-costs; Natural disasters; Unmanned aerial vehicles (UAV)