The field of autonomous UAV navigation is rapidly evolving, with a focus on developing innovative solutions to address the challenges of navigating in complex and dynamic environments. Recent research has emphasized the importance of robust and efficient methods for object detection, trajectory optimization, and collision avoidance. Notably, there is a growing trend towards leveraging machine learning and computer vision techniques to improve the accuracy and reliability of UAV navigation systems. Some notable papers in this area include: The paper on SF-TMAT, which proposes a novel teacher-student framework for UAV object detection in adverse scenes, demonstrating superior performance and generalization capabilities. The VisLanding paper, which presents a monocular 3D perception-based framework for safe UAV landing, showcasing enhanced accuracy and robustness in identifying safe landing zones. The Time-Optimized Safe Navigation paper, which introduces a fully onboard, real-time navigation system relying on lightweight sensors and novel visual depth estimation approaches, achieving state-of-the-art performance in computational efficiency and guaranteeing obstacle-free trajectories.