Remote sensing vehicle detection based on embedded system

Haoxiang Su; Zhenghong Dong; Fan Yang; Yu Lin

doi:10.1117/12.2588843

20 January 2021 Remote sensing vehicle detection based on embedded system

Haoxiang Su, Zhenghong Dong, Fan Yang, Yu Lin

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 1171903 (2021) https://doi.org/10.1117/12.2588843
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

At present, remote sensing image vehicle detection based on deep learning has achieved certain results, but most of them rely on powerful PC computing power and cannot be deployed in satellites, so they cannot provide support for satellite in-orbit detection. Aiming at this problem, this paper proposes a remote sensing image vehicle detection method based on YOLOv5 model and successfully deploys it in Jetson TX2 embedded equipment that can be deployed on a satellite platform. Experiments have proved that the algorithm proposed in this article detects vehicle targets in a 12000*12000 pixels wide remote sensing image in an embedded device, and the detection time is only about 1 minute and 20 seconds at the fastest.

Citation Download Citation

Haoxiang Su, Zhenghong Dong, Fan Yang, and Yu Lin "Remote sensing vehicle detection based on embedded system", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 1171903 (20 January 2021); https://doi.org/10.1117/12.2588843

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