Machine Learning and Artificial Intelligence Approaches for Drone Detection Using YOLOv11 Algorithm
Abstract
The rapid development of artificial intelligence (AI) and machine learning (ML) has significantly transformed the capabilities of unmanned aerial vehicle (UAV) detection systems. This study presents an AI- and ML-based approach for drone detection using the YOLOv11 algorithm, a state-of-the-art deep learning model designed for real-time object recognition. A custom dataset, consisting of 1450 drone images collected under diverse environmental and lighting conditions, was used to train and evaluate the model. The training process employed the YOLOv11 variants (n, s, m, l, x) on the PyTorch framework, with performance metrics including Precision, Recall, F1-Score, and mAP50–95. The results demonstrated exceptional detection accuracy, achieving up to 99% precision and 98% recall, with an overall mAP50 of 0.99 and mAP50–95 of 0.70. Loss function analyses indicated consistent convergence, while confusion matrix and confidence curve evaluations confirmed the model’s robustness in differentiating drone objects from background scenes. This research highlights the effectiveness of integrating deep learning architectures within AI-driven vision systems for UAV detection. The findings confirm that YOLOv11 offers a highly reliable and efficient solution for real-time drone identification, with strong potential for implementation in security, surveillance, and autonomous navigation applications.
Keywords
Artificial intelligence, deep learning, machine learning, image processing, object detection
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