Drone Detection in Restricted Areas Using Deep Learning

As the use of drones becomes more widespread, the corresponding rise in drone-related intrusions poses a growing threat to public safety and privacy. Traditional anti-drone systems typically rely on radio-frequency sensors for drone tracking. This paper investigates the fusion of deep learning-based detection algorithms with surveillance cameras within the framework of radio-frequency anti-drone systems. The primary aim is to assess the efficacy of contemporary models and training methodologies in achieving precise and real-time drone detection. One of the central challenges addressed in this paper is the detection of small drones at extended distances, coupled with the demand for real-time performance. To overcome the scarcity of small drone datasets, the research team constructed a real-world dataset for comprehensive evaluations. Various iterations of the YOLO (You Only Look Once) models were compared using this dataset, with specific modifications implemented to enhance small object detection. Additionally, the image sources are diversified for training, incorporating bird images to mitigate false positives and enhance model robustness. Among the YOLO models tested, YOLOv5 exhibited superior precision, recall, and F1 score. The work delves into the impact of additional detection layers on precision, recall, and F1 score, revealing trade-offs between these metrics. The inclusion of bird images in the background training process demonstrated improvements in accuracy and recall, underlining the importance of diverse training data. An intriguing finding was observed when excluding extremely small drones and birds from the analysis, resulting in heightened precision but diminished recall. This highlights the delicate balance required in optimizing detection algorithms for different scenarios. The paper also acknowledges the need for further investigation into the generalizability of the proposed approach across various drone types.