Application of Computer Vision Technology in UAV Path Planning

Drones are widely used in remote sensing, agriculture, logistics, and military fields due to their high mobility, low cost, and vertical take-off/landing advantages. Path planning, a core technology for autonomous operations, is limited by traditional GPS/inertial navigation in low-altitude, near-ground, or GPS-denied scenarios. Computer vision can recognize the real-time environment through on-board sensors without prerebuilt maps, so computer vision is a key solution. The integration of computer vision with drone path planning is reviewed in this paper and includes several key components: obstacle detection, visual SLAM, and multi-UAV dynamic path planning. Results show that computer vision has evolved from becoming a supportive sensor to the primary source of information and has drastically improved the safety and performance of drones in unknown and dynamic environments. Current challenges include on-board resource constraints, poor robustness in complex environments, and multi-UAV collaboration difficulties; future efforts will focus on lightweight models, algorithm generalization, and standardized collaboration. This research is crucial for promoting large-scale drone operations in smart cities and low-altitude economies, laying a technical foundation for wider applications in emergency rescue and precision agriculture.