Abstract
Unmanned aerial vehicles (UAVs), commonly referred to as drones, are extensively employed in various real-time applications, including remote sensing, disaster management and recovery, logistics, military operations, search and rescue, law enforcement, and crowd monitoring and control, owing to their affordability, rapid processing capabilities, and high-resolution imagery. Additionally, drones mitigate risks associated with terrorism, disease spread, temperature fluctuations, crop pests, and criminal activities. Consequently, this paper thoroughly analyzes UAV-based surveillance systems, exploring the opportunities, challenges, techniques, and future trends of drone technology. It covers common image preprocessing methods for drones and highlights notable one- and two-stage deep learning algorithms used for object detection in drone-captured images. The paper also offers a valuable compilation of online datasets containing drone-acquired photographs for researchers. Furthermore, it compares recent UAV-based imaging applications, detailing their purposes, descriptions, findings, and limitations. Lastly, the paper addresses potential future research directions and challenges related to drone usage
Publisher
Salud, Ciencia y Tecnologia