Abstract
Machine learning can extract the features of the target from the known image dataset and detect the target quickly by using the trained deep neural network. Due to UAVs’ fast flight speed and long detection distance, real-time and accurate target detection requirements are relatively high. For such scenarios,a ground object recognition and tracking method that integrates the YOLOv5 target detection algorithm and KCF target tracking algorithm are proposed. The combination of the two algorithms can meet the requirements of recognition speed, recognition rate, and tracking accuracy in the current scene. Two algorithms are run simultaneously in the recognition process, the detection frame with the highest confidence is selected as the final detection result, and the detected target is tracked by the KCF target tracking algorithm based on correlation filtering. The experimental results show that this method not only meets the requirements of recognition distance, recognition accuracy, and recognition speed of ground target but also can track a ground target in real-time, with strong robustness and real-time performance.
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