The Pytorch Implementation of Category-consistent deep network learning for accurate vehicle logo recognition

This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''.
Paper: Category-consistent deep network learning for accurate vehicle logo recognition
Authors: Wanglong Lu, Hanli Zhao, Qi He, Hui Huang, Xiaogang Jin

Framework Architecture

CCML_Learning

Requirements

  • Pytorch==1.0.1 or higher
  • opencv version: 4.1.0

Datasets

  • XMU:
    • Y. Huang, R. Wu, Y. Sun, W. Wang, and X. Ding, Vehicle logo recog775
      nition system based on convolutional neural networks with a pretraining strategy, IEEE Transactions on Intelligent Transportation Systems 16 (4) (2015) 1951-1960.
    • https://xmu-smartdsp.github.io/VehicleLogoRecognition.html
  • HFUT-VL1 and HFUT-VL2:
    • Y. Yu, J. Wang, J. Lu, Y. Xie, and Z. Nie, Vehicle logo recognition based on overlapping enhanced patterns of oriented edge magnitudes, Computers & Electrical Engineering 71 (2018) 273–283.
    • https://github.com/HFUT-VL/HFUT-VL-dataset
  • CompCars:
    • L. Yang, P. Luo, C. C. Loy, and X. Tang, A large-scale car dataset for
      fine-grained categorization and verification, in: Proc. IEEE Conference on
      Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp.
      3973-3981.
    • http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html
  • VLD-45:

VLF-net for classification (Vehicle logo feature extraction network)

  • Training with the classification pipeline

    • training XMU dataset
    python train.py --dataset_name XMU --framework Classification_Network
    • training HFUT-VL1 dataset
    python train.py --dataset_name HFUT_VL1 --framework Classification_Network
    • training HFUT-VL2 dataset
    python train.py --dataset_name HFUT_VL2 --framework Classification_Network
    • training CompCars dataset
    python train.py --dataset_name CompCars --framework Classification_Network
    • training VLD-45 dataset
    python train.py --dataset_name VLD-45 --framework Classification_Network

  • Testing with the classification pipeline

    • testing XMU dataset
    python test.py --dataset_name XMU --framework Classification_Network
    • testing HFUT-VL1 dataset
    python test.py --dataset_name HFUT_VL1 --framework Classification_Network
    • testing HFUT-VL2 dataset
    python test.py --dataset_name HFUT_VL2 --framework Classification_Network
    • testing CompCars dataset
    python test.py --dataset_name CompCars --framework Classification_Network
    • testing VLD-45 dataset
    python test.py --dataset_name VLD-45 --framework Classification_Network

VLF-net for category-consistent mask learning

  • Step 1:

    • Generation of the category-consistent masks. There are more details for the co-localization method PSOL.
    • Please note that we use the generated binary-masks directly instead of the predicted boxes.

  • Step 2:

    • After generating the category-consistent masks, we can further organize the training and testing data which are as below:
    root/
      test/
          dog/xxx.png
          dog/xxz.png
          cat/123.png
          cat/nsdf3.png
      train/
          dog/xxx.png
          dog/xxz.png
          cat/123.png
          cat/nsdf3.png
      mask/
          dog/xxx.png
          dog/xxz.png
          cat/123.png
          cat/nsdf3.png
    Note that each image has the corresponding generated category-consistent mask.

  • Step 3:

    • Now, you can training the model with the category-consistent mask learning framework

    • Training with the category-consistent deep network learning framework pipeline

      • training XMU dataset
      python train.py --dataset_name XMU --framework CCML_Network
      • training HFUT-VL1 dataset
      python train.py --dataset_name HFUT_VL1 --framework CCML_Network
      • training HFUT-VL2 dataset
      python train.py --dataset_name HFUT_VL2 --framework CCML_Network
      • training CompCars dataset
      python train.py --dataset_name CompCars --framework CCML_Network
      • training VLD-45 dataset
      python train.py --dataset_name VLD-45 --framework CCML_Network

    • Testing with the category-consistent deep network learning framework pipeline

      • testing XMU dataset
      python test.py --dataset_name XMU --framework CCML_Network
      • testing HFUT-VL1 dataset
      python test.py --dataset_name HFUT_VL1 --framework CCML_Network
      • testing HFUT-VL2 dataset
      python test.py --dataset_name HFUT_VL2 --framework CCML_Network
      • testing CompCars dataset
      python test.py --dataset_name CompCars --framework CCML_Network
      • testing VLD-45 dataset
      python test.py --dataset_name VLD-45 --framework CCML_Network

Experiments

Table3

Table4

Bibtex

  • If you find our code useful, please cite our paper:
    @article{LU2021,
title = {Category-consistent deep network learning for accurate vehicle logo recognition},
  journal = {Neurocomputing},
  year = {2021},
  issn = {0925-2312},
  doi = {https://doi.org/10.1016/j.neucom.2021.08.030},
  url = {https://www.sciencedirect.com/science/article/pii/S0925231221012145},
  author = {Wanglong Lu and Hanli Zhao and Qi He and Hui Huang and Xiaogang Jin}
  }

Acknowledgements

GitHub

https://github.com/LonglongaaaGo/CCML_Learning