License Plate Recognition Under the Dual Challenges of Sand and Light: Dataset Construction and Model Optimization
Source abstract
Abstract
License plate recognition in sandstorm conditions faces challenges such as image blurriness, reduced contrast, and partial information loss, which result in significant limitations in the feature extraction and recognition accuracy of existing methods. To address these challenges, this study proposes a license plate recognition method based on an improved AlexNetBN network. By introducing Batch Normalization (BN) layers, the model achieves greater training stability and generalization in complex environments. A dedicated dataset tailored for license plate recognition in sandstorm conditions was constructed, and data augmentation techniques were used to simulate real-world scenarios for model training and testing. Experimental results demonstrate that, compared to the traditional AlexNet model, AlexNetBN achieves higher recognition accuracy and robustness in environments with frequent sandstorms and significant variations in lighting intensity. This study not only effectively enhances license plate recognition performance under sandstorm conditions but also offers new insights and references for applying CNN-based methods in low-visibility scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle License Plate Recognition · Advanced Neural Network Applications · Advanced Steganography and Watermarking Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China
Xi’an Institute Electromechanical Information Technology, Xi’an 710065, China
Funding
National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities
References (41)
Show source-provided references
1. Research on multi-sensor fusion target detection algorithm in autonomous driving scenarios
2. Fu, G. (2014). Research and Application of Dynamic Prediction Model for Urban Intelligent Transportation. [Ph.D. Thesis, South China University of Technology].
3. A review of sandstorm weather and its research progress
4. SIFT meets CNN: A decade survey of instance retrieval10.1109/TPAMI.2017.2709749 ↗
5. Wang, X., Han, T.X., and Yan, S. (October, January 29). An HOG-LBP Human Detector with Partial Occlusion Handling. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan.10.1109/ICCV.2009.5459207 ↗
6. CSNet: A ConvNeXt-based Siamese network for RGB-D salient object detection10.1007/s00371-023-02887-x ↗
7. Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine10.1109/TII.2016.2645238 ↗
8. Hmidani, O., and Alaoui, E.M.I. (2022, January 12–14). A comprehensive survey of the R-CNN family for object detection. Proceedings of the 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet), Marrakech, Morocco.10.1109/CommNet56067.2022.9993862 ↗
9. Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019;, January 15–20). Libra r-cnn: Towards Balanced Learning for Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00091 ↗
10. Vehicle license plate detection using region-based convolutional neural networks10.1007/s00500-017-2696-2 ↗
11. Redmon, J. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
12. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
13. Redmon, J. (2018). Yolov3: An incremental improvement. arXiv.
14. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
15. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17–24). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗
16. Wang, G., Chen, Y., An, P., Hong, H., Hu, J., and Huang, T. (2023). UAV-YOLOv8: A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios. Sensors, 23.10.3390/s23167190 ↗
17. Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). Dssd: Deconvolutional single shot detector. arXiv.
18. Effects of Challenging Weather and Illumination on Learning-Based License Plate Detection in Noncontrolled Environments
19. Automatic license plate detection in hazardous condition10.1016/j.jvcir.2016.01.015 ↗
20. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
21. Ioffe, S. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv.
22. Zafar, A., Aamir, M., Mohd Nawi, N., Arshad, A., Riaz, S., Alruban, A., Dutta, A.K., and Almotairi, S. (2022). A comparison of pooling methods for convolutional neural networks. Appl. Sci., 12.10.3390/app12178643 ↗
23. Impact of fully connected layers on performance of convolutional neural networks for image classification10.1016/j.neucom.2019.10.008 ↗
24. Liu, W., Wen, Y., Yu, Z., and Yang, M. (2016). Large-margin softmax loss for convolutional neural networks. arXiv.
25. First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning10.1016/j.protcy.2014.09.007 ↗
26. Scherer, D., Müller, A., and Behnke, S. (2010). Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. International Conference on Artificial Neural Networks, Springer.10.1007/978-3-642-15825-4_10 ↗
27. A survey of the recent architectures of deep convolutional neural networks10.1007/s10462-020-09825-6 ↗
28. Nwankpa, C., Ijomah, W., Gachagan, A., and Marshall, S. (2018). Activation functions: Comparison of trends in practice and research for deep learning. arXiv.
29. Dropout vs. batch normalization: An empirical study of their impact to deep learning10.1007/s11042-019-08453-9 ↗
30. Yoshioka, T., Ito, N., Delcroix, M., Ogawa, A., Kinoshita, K., Fujimoto, M., Yu, C., Fabian, W.J., Espi, M., and Higuchi, T. (2015, January 13–17). The NTT CHiME-3 System: Advances in Speech Enhancement and Recognition for Mobile Multi-Microphone Devices. Proceedings of the 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Scottsdale, AZ, USA.10.1109/ASRU.2015.7404828 ↗
31. Representation visualization of convolutional neural networks: A survey
32. Research in Collaborative Tagging Applications: Choosing the Right Dataset10.21015/vtcs.v11i1.1305 ↗
33. Toward end-to-end car license plate detection and recognition with deep neural networks10.1109/TITS.2018.2847291 ↗
34. Laroca, R., Severo, E., Zanlorensi, L.A., Oliveira, L.S., Gonçalves, G.R., Schwartz, W.R., and Menotti, D. (2018, January 8–13). A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector. Proceedings of the 2018 International Joint Conference on Neural Networks (ijcnn), Rio de Janeiro, Brazil.10.1109/IJCNN.2018.8489629 ↗
35. Benchmark for license plate character segmentation10.1117/1.JEI.25.5.053034 ↗
36. Application-oriented license plate recognition10.1109/TVT.2012.2226218 ↗
37. A new CNN-based method for multi-directional car license plate detection10.1109/TITS.2017.2784093 ↗
38. Liu, X. (2019). Research on Face Image Recognition Technology Based on Deep Learning. [Ph.D. Thesis, University of Chinese Academy of Sciences (Changchun Institute of Optical Precision Machinery and Physics, Chinese Academy of Sciences)].
39. Yang, G., Pennington, J., Rao, V., Sohl-Dickstein, J., and Schoenholz, S.S. (2019). A mean field theory of batch normalization. arXiv.
40. Yong, H., Huang, J., Meng, D., Hua, X., and Zhang, L. (2020, January 23–28). Momentum Batch Normalization for Deep Learning with Small Batch Size. Proceedings of the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XII 16.10.1007/978-3-030-58610-2_14 ↗
41. Luo, P., Wang, X., Shao, W., and Peng, Z. (2018). Towards understanding regularization in batch normalization. arXiv.