Application and Challenges of Deep Learning in Object Detection for High-Altitude Observation Systems

High-altitude observation systems, as a critical type of video surveillance system, are widely employed in environmental monitoring, disaster warning, military reconnaissance, and other fields. However, current systems face challenges such as insufficient intelligent judgment and inadequate early warning capabilities. This paper systematically reviews the research status and development trends of visual analysis in high-altitude observation systems, covering key aspects such as system components, application performance, and object detection. It discusses the common components and application domains of high-altitude observation systems, analyzing unique challenges in target detection under high-altitude scenarios, including long monitoring ranges and adverse weather conditions. The application of deep learning-based object detection techniques in high-altitude imagery is also reviewed, emphasizing their effectiveness. Finally, the paper summarizes the main technical bottlenecks and future research directions in this field, including improvements in real-time performance and the construction of advanced image recognition models. This work aims to provide comprehensive references for researchers and promote the development and practical application of visual analysis technologies in high-altitude observation.