Vision-Based UAV Detection Methods Using Deep Learning: A Review
DOI:
https://doi.org/10.29304/jqcsm.2026.18.12504Keywords:
Drone Detection, UAV Detection,, UAV Datasets,, Deep Learning, Computer Vision.Abstract
Unmanned Aerial Vehicles (UAVs), or drones are increasingly used for civilian and military purposes. But their misuse raises serious concerns related to privacy, safety and security making them double-edged weapons. Consequently, there is an urgent need for effective UAV detection systems to mitigate threats posed by unauthorized UAV operations over restricted territories. With rapid advances in deep learning and computer vision, vision-based UAV detection systems have achieved notable progress. However, the existing reviews often lack systematic algorithmic analysis and clear summarization of trends and limitations. Therefore, this review aims to consolidate and summarize recent vision-based UAV detection methods using deep learning, focusing on convolutional neural network (CNN)-based models and to provide actionable directions for future research. Firstly, this study presents the evolution of UAV detection, key challenges and the pros and cons of the technologies used. Next it presents a summary of the recent advances in UAV detection methods utilizing one- or two-stage detectors only; the literature shows a strong dominance of YOLO-based architectures due to their favorable accuracy–speed trade-off and suitability for real-time deployment. It further summarizes commonly reported evaluation metrics (e.g., precision, recall and F1-score). Finally, it systematically reviews public UAV datasets and their characteristics highlighting persistent dataset limitations, including limited diversity in altitude, weather, illumination and environment which contributes to a comprehensive understanding of their characteristics and applicability.
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