This paper presents a novel CNN model called comparison prediction network for apparent age estimation. The algorithm is structured by feature extraction and Face feature database. Compared with the existing methods, our algorithm can better deal with the problem caused by differences between apparent age and actual age, which improves the prediction precision of the model with the increasing credibility and robustness of the model prediction results, enhancing the generalization ability of the model. The algorithm has fewer parameters and is lighter than other methods, which is suitable for mobile deployment.
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.