Objective The boring vibration recognition model based on deep learning combined with edge computing can realize the long-term monitoring and real-time warning of boring vibration of forest stem borers, but it needs to greatly reduce the parameters and computation of the intelligent recognition model. In this study, the deep learning model compression algorithms were adopted to compress the existing boring vibration recognition model without losing accuracy, so as to reduce the volume of the model and improve the recognition speed of the model in the embedded platform.
Method Firstly, two kinds of signals, i.e. the boring vibration of Semanotus bifasciatus and the background noise were collected, and used to train five-layer convolution neural network BoringNet to obtain the boring vibration recognition model. Then, the convolution filter pruning with different rates, model quantization and multi-target knowledge distillation were used to compress the boring vibration recognition model. Finally, the combination strategies of the above-mentioned compression algorithms were designed and three algorithms were combined to compress the boring vibration recognition model to explore the model compression effect of various combinations.
Result When the pruning rate was 60% under the combination of 3 model compression algorithms, the model was the best. At this time, the amount of calculation and parameters of the model were reduced from 18.06 ×106 and 0.54 ×106 to 3.01 ×106 and 0.09 ×106, respectively. The volume of the model was compressed from 2200 kB to 134.9 kB. The recognition time of raspberry pie 3B + was reduced from 9.04 ms to 1.65 ms, and the accuracy of model was still 99.29%, which was improved by 0.5%.
Conclusion The deep learning model compression method in this study can greatly compress the model parameters and computation for the boring vibration interception scene, realize the real-time recognition of the embedded platform on the premise of ensuring the accuracy, promote the transformation of boring vibration recognition model from workstation test to field deployment, and lay the foundation for edge computing of boring vibration recognition.