Unmanned aerial vehicles have gained significant importance in various fields, including surveillance, delivery services, disaster management, and aerial inspections. However, they still face challenges related to autonomy, including obstacle avoidance, accurate object detection, and efficient decision-making in dynamic environments. These challenges hinder their widespread adoption and limit their capabilities. To overcome these challenges, this abstract proposes the use of computer vision and AI deployment to enhance UAV autonomy. Computer vision techniques enable UAVs to perceive their surroundings accurately by analyzing the visual data captured by onboard cameras. AI algorithms enable UAVs to interpret this visual data, make intelligent decisions, and navigate through complex scenarios. The abstract highlights that by leveraging computer vision and AI, UAVs can autonomously detect and avoid obstacles, recognize objects of interest, and adapt their flight paths accordingly. These advancements in autonomy enable UAVs to operate safely and efficiently in various environments, including crowded urban areas, natural disaster zones, and industrial sites. The integration of computer vision and AI technologies in UAV operations also has the potential to improve data analysis and decision-making processes. UAVs equipped with AI algorithms can analyze vast amounts of visual data in real time, extract valuable insights, and provide actionable information to operators or other systems. Overall, this abstract emphasizes the importance of enhancing UAV autonomy through computer vision and AI deployment to overcome the current challenges faced globally. The proposed integration of these technologies has the potential to revolutionize UAV operations, enabling them to operate more effectively, safely, and autonomously in a wide range of applications.

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