Resumo:
The use of Unmanned Aerial Vehicles (UAVs) in search and rescue operations has grown
significantly, primarily due to reduced costs and lower associated risks. However, the
effectiveness of these vehicles is closely linked to the quality of the sensors used for target
capture and identification, making the investigation of these devices a crucial area of
research.
This study presents a systematic review of the literature on the application of Generative
Adversarial Networks (GANs) in UAV-generated images, with a focus on search and
rescue. Additionally, we introduce a methodology that uses the Real-ESRGAN tool to enhance
images obtained by UAVs during search and rescue missions, specifically targeting
sensors that operate in the infrared spectrum. The results of applying this technique to
our dataset show significant improvements in image quality, suggesting that this approach
may have valuable applications in post-processing and in the identification of human targets
in search and rescue operations.
