Lightweight Network Model for Moving Object Recognition

The convolutional neural network model with large volume and computation amount cannot be run by the embedded platform with small volume and limited resources, and the existing lightweight models are unable to juggle detection speed and accuracy. An object recognition algorithm of YOLO based on Ghost module (GS-YOLO) was proposed in this study. Following YOLOv4 model, the object recognition network was reconstructed based on Ghost module to reduce model parameters and convolution operations and improve object recognition rate. Multiple spatial pyramid pooling modules were incorporated to optimize the identification model and improve the accuracy of model identification. The channel pruning limit compression method was adopted to eliminate the redundant parameters and further reduce the model volume and calculation. The accuracy of pruning model was improved by fine tuning technique. Experimental data show that for the self-built test set and the same test environment, compared with YOLOv4 object recognition algorithm, GS-YOLO algorithm reduces the volume of YOLOv4 model by 96%, reduces the amount of floating point calculation by 91.2%, and increases the prediction speed by 2.9 times. After compression, the model recognition accuracy achieves 87.63%, and the accuracy loss is only 2.43%. © 2021, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.