As the core support for UAV autonomous flight, intelligent perception technology is driving the evolution of aircraft from remote control operation to fully autonomous decision-making. Through multi-source sensor fusion and deep learning algorithms, this technical system endows aircraft with environmental cognition and situation understanding capabilities, enabling them to independently complete navigation, obstacle avoidance and task execution in complex scenarios. Current research focuses on the collaborative optimization of key modalities such as visual perception, LiDAR, millimeter-wave radar and infrared detection, so as to improve the robustness of the system in low-visibility and high-dynamic environments. Technological breakthroughs have significantly expanded the application boundaries of UAVs in fields such as logistics distribution, agricultural plant protection, emergency rescue and infrastructure inspection, giving birth to new industrial forms and business models. However, the real-time bottleneck of perception systems, communication delay in multi-UAV coordination and reliability issues under complex meteorological conditions are still the main obstacles restricting large-scale deployment. The future development trend will point to the lightweight design of edge intelligent computing architecture, semantic-level fusion of multi-modal perception information, and the construction of distributed cooperative perception mechanisms for swarm intelligence, so as to achieve truly fully autonomous flight capabilities.