Jiang Panpan, Yang Xiaohua, Wan Yaping, Zeng Tiejun, Nie Mingxing, Liu Zhenghai
School of Nuclear Science and Technology, University of South China, Hengyang 421001, China.
Intelligent Nuclear Security Technology Laboratory, Hengyang 421001, China.
Sensors (Basel). 2024 Nov 7;24(22):7148. doi: 10.3390/s24227148.
Interest in anti-UAV detection systems has increased due to growing concerns about the security and privacy issues associated with unmanned aerial vehicles (UAVs). Achieving real-time detection with high accuracy, while accommodating the limited resources of edge-computing devices poses a significant challenge for anti-UAV detection. Existing deep learning-based models for anti-UAV detection often cannot balance accuracy, processing speed, model size, and computational efficiency. To address these limitations, a lightweight and efficient anti-UAV detection model, DRBD-YOLOv8, is proposed in this paper. The model integrates several innovations, including the application of a Re-parameterization Cross-Stage Efficient Layered Attention Network (RCELAN) and a Bidirectional Feature Pyramid Network (BiFPN), to enhance feature processing capabilities while maintaining a lightweight design. Furthermore, DN-ShapeIoU, a novel loss function, has been established to enhance detection accuracy, and depthwise separable convolutions have been included to decrease computational complexity. The experimental results showed that the proposed model outperformed YOLOV8n in terms of mAP50, mAP95, precision, and FPS while reducing GFLOPs and parameter count. The DRBD-YOLOv8 model is almost half the size of the YOLOv8n model, measuring 3.25 M. Its small size, fast speed, and high accuracy combine to provide a lightweight, accurate device that is excellent for real-time anti-UAV detection on edge-computing devices.
由于对与无人机（UAV）相关的安全和隐私问题的担忧日益增加，对反无人机检测系统的兴趣也随之上升。在适应边缘计算设备有限资源的同时实现高精度的实时检测，对反无人机检测构成了重大挑战。现有的基于深度学习的反无人机检测模型往往无法在准确性、处理速度、模型大小和计算效率之间取得平衡。为了解决这些限制，本文提出了一种轻量级且高效的反无人机检测模型DRBD-YOLOv8。该模型集成了多项创新，包括应用重新参数化跨阶段高效分层注意力网络（RCELAN）和双向特征金字塔网络（BiFPN），以增强特征处理能力，同时保持轻量级设计。此外，还建立了一种新颖的损失函数DN-ShapeIoU来提高检测准确性，并采用深度可分离卷积来降低计算复杂度。实验结果表明，所提出的模型在mAP50、mAP95、精度和FPS方面优于YOLOV8n，同时减少了GFLOPs和参数数量。DRBD-YOLOv8模型的大小几乎是YOLOv8n模型的一半，为3.25M。它体积小、速度快、精度高，结合起来提供了一种轻量级、准确的设备，非常适合在边缘计算设备上进行实时反无人机检测。
