Object Detection Techniques in UAV Aerial Imaging Scenarios

With the rapid development of computer vision technology, object detection in drone-captured imagery has become a highly important research topic in recent years.This technology is now applied in a wide range of fields, including urban planning, agricultural monitoring, disaster response, traffic management, and military reconnaissance.However, using drones for object detection still presents several major challenges.Targets in these images are often very small, backgrounds are typically complex and contain many distracting elements, and the sizes of objects can vary significantly.Additionally, lighting and weather conditions frequently change, and occlusion is a common issue.All of these factors make it difficult to achieve accurate and efficient object detection.This paper begins by introducing two primary categories of object detection models based on deep learning: single-stage and two-stage models.It then examines the main technical challenges encountered in real-world drone-based detection and proposes strategies for improvement.Finally, drawing on recent research, the paper offers recommendations for further enhancing object detection methods.