Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study

Fang Huanga School of Recourses and Environment, University of Electronic Science and Technology of China (UESTC), Chengdu, People’s Republic of China

https://orcid.org/0000-0002-5051-3061 View further author information

Shengyi Chena School of Recourses and Environment, University of Electronic Science and Technology of China (UESTC), Chengdu, People’s Republic of China

https://orcid.org/0000-0002-8871-7713 View further author information

Qi Wangb Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, People’s Republic of China

https://orcid.org/0009-0007-9573-7724 View further author information

Yingjie Chena School of Recourses and Environment, University of Electronic Science and Technology of China (UESTC), Chengdu, People’s Republic of ChinaView further author information

Dandan Zhangc School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaCorrespondence[email protected]

https://orcid.org/0000-0001-8025-3566 View further author information

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Zhe Chen, Yuan Liang, Zhengbo Yu, Ke Xu, Qingyun Ji, Xueqi Zhang, Quanping Zhang, Zijia Cui, Ziqiong He, Ruichun Chang, Zhongchang Sun, Keyan Xiao & Huadong Guo. (2023) TO–YOLOX: a pure CNN tiny object detection model for remote sensing images. International Journal of Digital Earth 16:1, pages 3882-3904.
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Michael Abebe Berwo, Asad Khan, Yong Fang, Hamza Fahim, Shumaila Javaid, Jabar Mahmood, Zain Ul Abideen & Syam M.S.. (2023) Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey. Sensors 23:10, pages 4832.
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Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study

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Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study

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