Feature Extraction Network SFPN for Edge Computing Scenarios

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Abstract
Aiming at the problems that the existing networks do not adapt to the image characteristics of edge scenes, do not effectively process the detail information in images and have insufficient feature fusion, this thesis proposes a feature extraction network model SFPN that can better utilize the different depths and detail semantic information of the network. Firstly, the S-soft unit is designed to process the detail information of the feature map. Secondly, this thesis proposes a new secondary fusion strategy that combines the features of the middle layer are fused with the lower and higher layers respectively, and with this, SFPN Block is proposed as an auxiliary classifier, which participates in the final prediction by weighting, fully considering the prediction information of different layers of the network. Finally, a joint loss function is introduced for training, so that the feature extraction ability of the network for targets with large scale changes and small sizes is improved, and it is more adapted to the edge computing scenario of detection requirements and improve the accuracy of feature extraction.
Funding Information
  • the Fundamental Research Funds for the Central Universities (2023QN1079)
  • the National Natural Science Foundation of China (NSFC) ((52274160, 51874300, 52074305))
  • the Graduate Innovation Program of China University of Mining and Technology (2023WLKXJ172)