YOLO-Drone: An Optimized YOLOv8 Network for Tiny UAV Object Detection

无人机 计算机科学 目标检测 人工智能 特征提取 特征(语言学) 骨干网 精确性和召回率 计算机视觉 模式识别(心理学) 实时计算 计算机网络 语言学 哲学 遗传学 生物
作者
Xianxu Zhai,Huang Zhi-hua,Tao Li,Hanzheng Liu,Siyuan Wang
出处
期刊:Electronics [Multidisciplinary Digital Publishing Institute]
卷期号:12 (17): 3664-3664 被引量:31
标识
DOI:10.3390/electronics12173664
摘要

With the widespread use of UAVs in commercial and industrial applications, UAV detection is receiving increasing attention in areas such as public safety. As a result, object detection techniques for UAVs are also developing rapidly. However, the small size of drones, complex airspace backgrounds, and changing light conditions still pose significant challenges for research in this area. Based on the above problems, this paper proposes a tiny UAV detection method based on the optimized YOLOv8. First, in the detection head component, a high-resolution detection head is added to improve the device’s detection capability for small targets, while the large target detection head and redundant network layers are cut off to effectively reduce the number of network parameters and improve the detection speed of UAV; second, in the feature extraction stage, SPD-Conv is used to extract multi-scale features instead of Conv to reduce the loss of fine-grained information and enhance the model’s feature extraction capability for small targets. Finally, the GAM attention mechanism is introduced in the neck to enhance the model’s fusion of target features and improve the model’s overall performance in detecting UAVs. Relative to the baseline model, our method improves performance by 11.9%, 15.2%, and 9% in terms of P (precision), R (recall), and mAP (mean average precision), respectively. Meanwhile, it reduces the number of parameters and model size by 59.9% and 57.9%, respectively. In addition, our method demonstrates clear advantages in comparison experiments and self-built dataset experiments and is more suitable for engineering deployment and the practical applications of UAV object detection systems.
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