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5 November 2020 Remote sensing object detection based on YOLO and embedded systems
Yu Lin, Zhenghong Dong, Lurui Xia, Junwei Wang
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Proceedings Volume 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics; 115650T (2020) https://doi.org/10.1117/12.2579806
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China
Abstract
Remote sensing image object detection is the primary task in the field of intelligent processing and has important practical application value. However, the current intelligent processing method of remote sensing images is difficult to meet the real-time requirements. In order to improve the effectiveness, real-time on-board processing of the collected images has become an important direction. This article compares several commonly used deep learning object detection algorithms, selects YOLO v3 for in-depth research and optimization, and transplants the debugged algorithm to the Cambrian 1H8 embedded edge intelligent computing platform for performance testing. Experiments show that the algorithm has high accuracy and the running speed basically meets the real-time requirements. It can be used to study and test the real-time processing performance of remote sensing images.
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Yu Lin, Zhenghong Dong, Lurui Xia, and Junwei Wang "Remote sensing object detection based on YOLO and embedded systems", Proc. SPIE 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics, 115650T (5 November 2020); https://doi.org/10.1117/12.2579806
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KEYWORDS
Remote sensing
Detection and tracking algorithms
Image processing
Embedded systems
Satellites
Target detection
Satellite imaging
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