Advertisement

Vehicle Logo Detection Based on Modified YOLOv2

  • Shuo Yang
  • Chunjuan Bo
  • Junxing ZhangEmail author
  • Meng Wang
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Vehicle logo detection technology is one of the research directions in the application of intelligent transportation systems. It is an important extension of detection technology based on license plates and motorcycle types. A vehicle logo is characterized by uniqueness, conspicuousness, and diversity. Therefore, thorough research is important in theory and application. Although numerous vehicle logo detection methods exist, most of them cannot achieve real-time detection for different scenes. The YOLOv2 network is improved by constructing the data of a vehicle logo, dimension clustering of the bounding box, reconstructing network pre-training, and multi-scale detection training. This work implements fast and accurate vehicle logo detection. The generalization of the detection model and anti-interference capability in real scenes are optimized by data enrichment. The experimental results show that the accuracy and speed of the detection algorithm are improved.

Keywords

Vehicle logo detection Reality scene YOLOv2 Data conformation 

Notes

Acknowledgments

This work is supported by National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2015BAD29B01), Key Research Guidance Plan Project of Liaoning Province (No. 2017104013), Natural Science Foundation of Liaoning Province (No. 201700133), and Fundamental Research Funds of Central University (No. 0102-20000101).

References

  1. 1.
    Du, S., Ibrahim, M., Shehata, M., et al. (2013). Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions on Circuits and Systems for Video Technology, 23(2), 311–325.CrossRefGoogle Scholar
  2. 2.
    Psyllos, A. P., & Kayafas, E. (2010). Vehicle logo recognition using a SIFT – Based enhanced matching scheme. IEEE Transactions on Intelligent Transportation Systems, 11, 322–328.CrossRefGoogle Scholar
  3. 3.
    Llorca, D. F., Arroy, O. R., & Sotelo, M. A. (2013). Vehicle logo recognition in traffic images using hog features and SVM. In IEEE Conference on Intelligent Transportation Systems (pp. 2229–2234).Google Scholar
  4. 4.
    Sun, Q., Lu, X., Chen, L., et al (2014). An improved vehicle logo recognition method for road surveillance images. In IEEE Proceedings of the 2014 Seventh International Symposium on Computational Intelligence and Design (pp. 373–376).Google Scholar
  5. 5.
    Sam, K. T., & Tian, X. L. (2012). Vehicle logo recognition using modest AdaBoost and radial Tchebichef moments. In International Conference on Machine Learning and Computing (pp. 91–95).Google Scholar
  6. 6.
    Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, 60(2), 1097–1105.Google Scholar
  7. 7.
    H, L., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Application, 23(2), 368–375.CrossRefGoogle Scholar
  8. 8.
    Serikawa, S., & Lu, H. (2014). Underwater image dehazing using joint trilateral filter. Computers and Electrical Engineering, 40(1), 41–50.CrossRefGoogle Scholar
  9. 9.
    Lu, H., Li, Y., Uemura, T., Kim, H., & Serikawa, S. (2018). Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Generation Computer Systems, 82, 142–148.CrossRefGoogle Scholar
  10. 10.
    H, L., Li, Y., S, M., Wang, D., Kim, H., & Serikawa, S. (2018). Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet of Things Journal, 5(4), 2315–2322.CrossRefGoogle Scholar
  11. 11.
    H, L., Li, B., Zhu, J., Li, Y., et al. (2017). Wound intensity correction and segmentation with convolutional neural networks. Concurrency and Computation: Practice and Experience, 29(6), e3927.CrossRefGoogle Scholar
  12. 12.
    X, X., He, L., H, L., Gao, L., & Ji, Y. (2019). Deep adversarial metric learning for cross-modal retrieval. World Wide Web, 22(2), 657–672.  https://doi.org/10.1007/s11280-018-0541-x.CrossRefGoogle Scholar
  13. 13.
    Li, P., Wang, D., Wang, L., & Lu, H. (2017). Deep visual tracking: Review and experimental comparison. Pattern Recognition, 76, 323–338.CrossRefGoogle Scholar
  14. 14.
    Huang, Y., Wu, R., Sun, Y., Wang, W., & Ding, X. (2015). Vehicle logo recognition system based on convolutional neural networks with a pretraining strategy. IEEE Transactions on Intelligent Transportation Systems, 16(4), 1951–1960.CrossRefGoogle Scholar
  15. 15.
    Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(6), 1137.CrossRefGoogle Scholar
  16. 16.
    Tang, T., Zhou, S., Deng, Z., et al. (2017). Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining. Sensors, 17(2), 336.CrossRefGoogle Scholar
  17. 17.
    Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779–788).Google Scholar
  18. 18.
    Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7263–7271).Google Scholar
  19. 19.
    Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 283–304.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shuo Yang
    • 1
  • Chunjuan Bo
    • 2
    • 3
  • Junxing Zhang
    • 1
    Email author
  • Meng Wang
    • 1
  1. 1.College of Electromechanical EngineeringDalian Minzu UniversityDalianChina
  2. 2.College of Information and Communication EngineeringDalian Minzu UniversityDalianChina
  3. 3.Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs CommissionDalian Minzu UniversityDalianChina

Personalised recommendations