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\% $IoU$.
翻译:在每一个部门,无人驾驶飞行器(无人驾驶飞行器)的技术进展已正常化,从军事到商业,从军事到商业,它们也造成严重的安全关切,因为它们的功能得到增强,很容易进入私人和高度安全的地区。与无人驾驶飞行器有关的一些事例引起了安全关切,导致无人驾驶飞行器的探测研究。视觉技术被广泛采用,用于无人驾驶飞行器的探测,但在夜间、复杂背景和恶劣的天气条件下表现不佳。因此,需要有强有力的夜视无人驾驶飞行器探测系统,才能有效地解决这一问题。红外摄影机越来越多地用于夜间监视,因为它们在夜视设备中广泛应用。本文使用了基于深层次学习的细微功能网络(TF-Net),这是YOLOv5的改进版本,以便使用红外(IR)图像准确地探测夜间无人驾驶飞行器,但在拟议的TF-O5的颈部和脊椎骨架上,我们还模拟了四种不同的YOLOV5模型(s,m,n,l)和拟议的TF-Net(44-O)的精确度,比起来,YOL-O5。