In response to the low detection rate and slow response time in low-altitude UAV detection,a real-time intelligent detection system for low-altitude UAVs has been designed and implemented.This system includes a complete workflow consisting of monitoring data collection,video data processing,UAV dataset creation,pre-trained models,transfer learning training,analysis of training results,and system integration and demonstration.Key performance indicators for UAV detection scenes were analyzed and determined.An estimation of the model parameter quantity based on YOLOv5 was conducted.Furthermore,the process of UAV detection and path tracking was demonstrated in a real-world scenario with a 3D map.Experimental data indicated that,under the condition of a specified minimum recall rate of 90％and a confidence level of 53％,the corresponding precision rate was 96％.Moreover,the system integrated and operated normally in a campus test environment,meeting the requirements of real-time intelligent detection for low-altitude UAV scenes.
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