Lightweight Air Quality Detection Method Based on Channel Pruning
Aiming at the problem of complex structure,difficult deployment and high cost of traditional air quality detection system,a lightweight air quality detection algorithm based on channel pruning was proposed using graph convolutional network to analyze atmospheric image features.Firstly,a PM2.5 index detection network was trained based on ResNet50 to achieve prelimina-ry automated detection of air quality.Then,all convolutional kernel channels and related parameter transfers in the network model were used for graph node kernel weight edge modeling,which input the graph convolutional network in the form of a graph repre-sentation and output pruning importance discrimination predictions for each convolutional kernel node.Finally,the channel prun-ing was performed according to the GCN results,and the original dataset was used to fine tune the parameters of the model after pruning,which achieved lightweight network models while maintaining network detection accuracy.The ablation experiments and comparative experiments verify that the proposed detection method has higher detection accuracy,the average detection error is only 5.31%,the RMSE is improved by 0.52,while R-square is only reduced by 0.018,which also solves the problem of exce-ssive parameter and computational complexity in the network model.The network parameter quantity is decreased from 4.12×107 to 2.01×107,and the FPS increased from 16.78 frame/s to 30.9 frame/s,which provides strong technical support for achieving air quality detection tasks on portable terminals.
air quality monitoringatmospheric imagechannel pruningconvolution kernel channelgraph convolutional networknetwork lightweight
NETL
NSTL
万方数据
维普
