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Customization and optimization of SSD-based neural network model for detection of external force damage on transmission lines
Author(s): Yingying Chi; Rui Liu; Wenpeng Cui; Haifeng Zhang; Yidong Yuan
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Paper Abstract

Based on the principle of SSD (Single Shot Multibox Detector) convolutional neural network algorithm, this paper develops corresponding training strategies, and uses the source data generated under a large number of power-grid scenarios to train and generate a 100-megabyte neural network model for intelligent monitoring of external force damage on transmission lines. Using the deep compression technology, the trained neural network model is re-trained and optimized in a targeted manner to ensure a compression ratio of 30%-50% under the premise that the accuracy is not degraded. In this way, the hardware storage resource configuration is more reasonable when the model is deployed on the embedded platform.

Paper Details

Date Published: 31 July 2019
PDF: 6 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980R (31 July 2019); doi: 10.1117/12.2540376
Show Author Affiliations
Yingying Chi, Smart-Chip Microelectronics Technology Co., Ltd. (China)
Rui Liu, Smart-Chip Microelectronics Technology Co., Ltd. (China)
Wenpeng Cui, Smart-Chip Microelectronics Technology Co., Ltd. (China)
Haifeng Zhang, Smart-Chip Microelectronics Technology Co., Ltd. (China)
Yidong Yuan, Smart-Chip Microelectronics Technology Co., Ltd. (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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