The recent advancements in the field of Unmanned Aerial Vehicle (UAV) perception have significantly shifted towards enhancing 3D and collaborative 3D perception tasks. Researchers are increasingly focusing on developing benchmarks and models that can effectively handle 3D object detection and tracking, addressing the limitations of traditional 2D perception tasks. This shift is crucial for real-world applications where a comprehensive 3D understanding of the environment is essential, such as in aerial photography, surveillance, and agriculture. Additionally, there is a notable emphasis on improving the detection of small and occluded objects, which has been a longstanding challenge in UAV applications. Attention-based models and synthetic data integration are emerging as key strategies to enhance detection accuracy and reduce false positives. These innovations not only improve the performance of defect detection models but also streamline the data labeling process by leveraging synthetic imagery. Furthermore, advancements in spatiotemporal object detection are being explored to enhance the detection of vehicles in traffic monitoring scenarios, demonstrating the potential for further performance gains through the integration of temporal dynamics and attention mechanisms. Overall, the field is progressing towards more sophisticated and integrated solutions that promise to significantly advance the capabilities of UAVs in various applications.