Enhancing Information Fusion from UAV-Captured High-Altitude Infrared Imagery through Machine Learning

Mustafa El-Taie ¹, Aaras Y. Kraidi ^2,*

¹ Digital Charging Solutions GmbH, Germany  

² University of Technology and Applied Science, Shinas, Oman  

Emails: Mustafa.iessa@gmail.com;  aaras.kraidi@shct.edu.om 

Abstract

Unmanned aerial vehicles (UAVs) equipped with high-altitude infrared imaging have revolutionized data collection, providing better spatial and temperature resolutions. However, an effective way to fuse and interpret this multidimensional data remains a challenge. Therefore, this research tackles this issue by incorporating machine learning specifically the YOLO object detector to fuse and analyze information from UAV-captured high-altitude infrared images. The process entails a careful fusion of data, feature extraction, and model configuration that is tailored to the unique qualities of infrared imagery. Furthermore, the confabulated YOLO model performs exceptionally well in detecting and localizing objects within the thermal spectrum. Results showed precise identification of objects as well as their localization thus indicating potential for advanced aerial surveillance and monitoring. This research represents a significant advancement in situation awareness across environmental monitoring, infrastructure inspection, and disaster response among other areas hence demonstrating the transformative ability of machine learning in aerial imaging analysis.

Keywords: Machine Learning; Unmanned Aerial Vehicles; Infrared Image Processing; Information Fusion; Remote Sensing; Image Data Integration; Sensor Fusion.