Authors:
Dapinder Kaur
1
;
2
;
Neeraj Battish
1
;
Arnav Bhavsar
3
and
Shashi Poddar
1
;
2
Affiliations:
1
CSIR – Central Scientific Instruments Organisation, Sector 30C, Chandigarh 160030, India
;
2
Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, India
;
3
IIT Mandi, Himachal Pradesh 175005, India
Keyword(s):
Deep Learning, UAVs, YOLOv7, Attention Modeling, Air-to-Air Object Detection.
Abstract:
The detection of Unmanned Aerial Vehicles (UAVs) is a special case for object detection, specifically in the case of air-to-air scenarios with complex backgrounds. The proliferated use of UAVs in commercial, noncommercial, and defense applications has raised concerns regarding their unauthorized usage and mishandling in certain instances. Deep learning-based architectures developed recently to deal with this challenge could detect UAVs very efficiently in different backgrounds. However, the problem of detecting UAVs in complex background environments need further improvement and has been catered here by incorporating an attention mechanism in the YOLOv7 architecture, which considers channel and spatial attention. The proposed model is trained with the DeTFly dataset, and its performance has been evaluated in terms of detection rate, precision, and mean average precision values. The experimental results present the effectiveness of the proposed YOLOv7E architecture for detecting UAVs
in aerial scenarios.
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