TF-Net: Deep Learning Empowered Tiny Feature Network for Night-time UAV Detection

• URL: http://arxiv.org/abs/2211.16317v1
• Date: Tue, 29 Nov 2022 15:58:36 GMT
• Title: TF-Net: Deep Learning Empowered Tiny Feature Network for Night-time UAV Detection
• Authors: Maham Misbah and Misha Urooj Khan and Zhaohui Yang and Zeeshan Kaleem
• Abstract summary: This paper uses a deep learning-based TinyFeatureNet (TF-Net) to accurately detect UAVs during the night using infrared (IR) images. The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS.
• Score: 10.43480599406243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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\% $IoU$.

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