Optimizing the Performance of UAV-Based Searching Missing Persons Process Using Deep Learning

Searching missing persons in forest is a challenging for rescuers because of lack of guidance in area and internet connectivity. Dense forest makes it difficult for rescuers while exploring the area using any method because some time it is difficult to capture presence of human in forest area due to density (sparse) of forest area. Previous techniques using GPS and RFID searching methods sometime does not produce accurate results. For this purpose, a new technique is proposed in this research paper a UAV-Based searching missing persons process in dense forests that uses deep learning technique applied on data collected in real time to optimize the performance and searching operation with high accuracy. Searching operation needs high efficiency, reliability, and accuracy as to fast response time. So, in this research new optimized technique is purposed for efficiency in search operation. With the development artificial intelligence and drone technology Unmanned aerial vehicles (UAVs) are become increasingly popular for used in SAR operations. UAV has the overcome the issue faced in forest area search and rescue operation like fixed coverage and reach out difficult accessible areas. Therefore, it will help with for collecting missing persons data then deep learning is applied to collected images to extract human features from collected information or dataset [5]. Experiment is performed to check performance of this method that results in promised output.