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

• Published in: Electronics (Basel) Vol. 12; no. 17; p. 3664
• Main Authors: Zhai, Xianxu, Huang, Zhihua, Li, Tao, Liu, Hanzheng, Wang, Siyuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Accuracy; Airports; Algorithms; Altitude; Datasets; Deep learning; Drone aircraft; Drones; Feature extraction; High resolution; Image processing; Industrial applications; Mathematical models; Methods; Neural networks; Object recognition; Object recognition (Computers); Parameters; Pattern recognition; Performance enhancement; Public safety; Redundancy; Target detection; Unmanned aerial vehicles; China
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12173664

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.