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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.