Analysis of the Real-Time Image Recognition and Classification Technology for UAVs on the Basis of YOLOv5

This paper addresses the challenges of real-time image recognition and classification encountered by unmanned aerial vehicles (UAVs) in complex environments. These challenges include inadequate target detection accuracy, slow data processing, and limited adaptability. To tackle these issues, this study proposes a single target tracking algorithm based on YOLOv5s. First, a real-time image recognition system is developed according to the YOLOv5 network model, and the technical schemes of key links, such as UAV image acquisition, data preprocessing, model training, and parameter optimization, are obtained. Second, a data transmission protocol specification specifically designed for UAV application scenarios is proposed to improve recognition efficiency and accuracy. Third, considering the characteristics of the YOLOv5 model structure, a model optimization framework that considers real-time and accuracy is constructed to accurately describe the UAV image recognition process. Finally, this study builds a UAV real-time image recognition system based on the YOLOv5s algorithm, and simulation and experimental verification prove that the proposed algorithm can effectively improve the target detection ability of UAVs in complex environments and offer theoretical and technical support for subsequent research.