Research on Real-Time Monitoring Algorithm for Edge-Based Aviation Meteorological Disasters Integrating Multi-source Data

Unmanned aerial vehicles (UAVs) can take advantage of cutting-edge precision provided by artificial intelligence (AI), particularly when it comes to deep learning (DL) focused methods. This could improve the UAVs’ remote sensing features for a variety of evacuation and disaster prevention possibilities. To lessen the impacts of disasters on the surroundings and human beings as quickly as possible, UAVs with camera lenses can operate in distant and challenging-to-access disaster-affected areas, examine the images, and sound alerts when different disasters, like collapsed structures, floods, or fires, occur. The implementation of these kinds of neural systems in numerous contexts requiring low-delay boundaries on deduction and mission-critical choices in real-time is hindered by the hefty processing needs introduced by the incorporation of DL. For disaster mitigation and monitoring uses, this paper presents a unique atrous-residual fusion network (ARFN) for effective aerial image categorization from drones. To train the proposed method, multiple sensed samples are acquired and processed on the Python platform. The performance of the proposed method is analyzed and compared with other existing methods. The investigation is conducted using a Python tool, and performance metrics such as F1-score (94.9%), recall (95.6%), accuracy (92.7%) and precision (94.6%) are utilized to evaluate the research’s effectiveness. To handle multiple resolution attributes on low-power systems effectively and increase efficiency, the design suggested makes use of atrous convolutions. From the experimental results, it can be concluded that the proposed approach attains the greatest performance of the existing methods in disaster management.