Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250400017-8.doi: 10.11896/jsjkx.250400017

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Vehicle Re-identification Based on RWM and Multi-scale Attention

LI Yalong1, WANG Hairui1, ZHU Guifu2,3, LU Shiyu1   

  1. 1 School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650504,China
    2 Information Construction Management Center,Kunming University of Science and Technology,Kunming 650504,China
    3 Kunming University of Science and Technology Shuguang Information Industry Co.,Ltd.,AI Joint Research Center,Kunming 650504,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:LI Yalong,born in 2000,postergra-duate.His main research interests include computer vision and vehicle re-identification
    ZHU Guifu,born in 1984,supervisor,senior engineer.His main research interests include Intelligent diagnosis technology,education big data.
  • Supported by:
    National Natural Science Foundation of China(62462064,61863106).

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Abstract: This paper proposes a vehicle re-identification method based on RWM and multi-scale attention to address the pro-blems of large intra class differences and high inter class similarity in existing vehicle re-identification tasks,which lead to insufficient key feature extraction and fusion of global and local features.Firstly,a Region Weighted Mapping(RWM) is designed to enhance the feature representation of key regions in the image,effectively reducing the interference of background information.Se-condly,based on the self attention mechanism of the Transformer structure,a multi-scale attention module(MAB) is introduced,which combines the large kernel receptive field and multi-scale characteristics to effectively model global structural information,while enhancing the expression ability of local details and improving the model's discriminative ability.Finally,a mixed loss function is constructed to optimize the feature learning process of the model,making the features of different categories of vehicles more distinguishable and improving generalization ability.Experiments on the proposed method are conducted on the VeRi-776 and VehicleID datasets.The CMC@1 values reach 97.4% and 85.8% respectively,while the CMC@5 values reach 98.9% and 97.7% respectively.The results show that the proposed method can extract more discriminative vehicle features.

Key words: Vehicle re-identification, Regional weighted mapping, Multi-scale large attention, Large nuclear receptive field

CLC Number: 

  • TP391
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