Intelligent recognition and tracking algorithm based on battlefield environment

Unmanned Aerial Vehicle (UAV) technology plays a pivotal role in military reconnaissance and battlefield surveillance. With the rapid advancement of deep learning, the integration of deep learning with UAV technology has become increasingly significant. Addressing the current shortfall in autonomous recognition capabilities of UAV technology in complex environments, this paper proposes an intelligent recognition and tracking algorithm for UAVs based on battlefield conditions. Building upon a specially curated dataset for friend-or-foe target recognition under various environments, this study combines the YOLO algorithm with PID control methods to develop a UAV control system capable of classifying target behaviors and achieving real-time tracking and ranging during dynamic flight. Notably, the friend-or-foe target recognition dataset is enhanced with multi-dimensional labels for friend-or-foe identification, enemy situation analysis, gesture control, and formation behaviors, providing a rich set of training data. Experimental results demonstrate that the target recognition classification achieved an accuracy of 91.2%, with a response time within 0.2 seconds, thereby confirming the algorithm's effectiveness and robustness under diverse battlefield conditions.