论文标题
TF-NET：深度学习授权的小型功能网络用于夜间无人机检测
TF-Net: Deep Learning Empowered Tiny Feature Network for Night-time UAV Detection
论文作者
论文摘要
技术进步已使每个部门的无人机（UAV）使用正常化，从军事到商业，但由于其功能增强并易于进入私人和高度安全的地区，它们也引起了严重的安全问题。与无人机有关的几个实例引起了安全问题，导致了无人机检测研究。视觉技术被广泛用于无人机检测，但在复杂的背景和不利天气条件下，它们在晚上的表现较差。因此，需要有效解决此问题的强大基于夜视的无人机检测系统。红外摄像机由于其在夜视设备中的广泛应用而越来越多地用于夜间监视。本文使用基于深度学习的TinyFeaturenet（TF-NET），它是改进的Yolov5s版本，用于使用红外（IR）图像在夜间准确检测无人机。在拟议的TF-NET中，我们在Yolov5s的颈部和骨干上引入了建筑变化。我们还模拟了四种不同的Yolov5模型（S，M，N，L），并提出了TF-NET，以进行公平比较。与Yolov5s相比，从精确度，IOU，GFLOPS，模型大小和FPS方面，提出的TF-NET的性能更好。 TF-NET以95.7 \％的精度，84 \％地图和44.8 \％$ iou $产生了最佳结果。
Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV detection research studies. Visual techniques are widely adopted for UAV detection, but they perform poorly at night, in complex backgrounds, and in adverse weather conditions. Therefore, a robust night vision-based drone detection system is required to that could efficiently tackle this problem. Infrared cameras are increasingly used for nighttime surveillance due to their wide applications in night vision equipment. This paper uses a deep learning-based TinyFeatureNet (TF-Net), which is an improved version of YOLOv5s, to accurately detect UAVs during the night using infrared (IR) images. In the proposed TF-Net, we introduce architectural changes in the neck and backbone of the YOLOv5s. We also simulated four different YOLOv5 models (s,m,n,l) and proposed TF-Net for a fair comparison. The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS compared to the YOLOv5s. TF-Net yielded the best results with 95.7\% precision, 84\% mAp, and 44.8\% $IoU$.
