지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.5
- 발행연도
- 2023.10
- 수록면
- 428 - 433 (6page)
- DOI
- 10.5573/IEIESPC.2023.12.5.428
이용수
초록· 키워드
오류제보하기
In intelligent transportation systems, accurate license plate recognition is an important component. This paper briefly introduces the LeNet-5 model for license plate image recognition. We improved the model by introducing an inception-SE convolution module. In simulation experiments, the optimized LeNet-5 model was compared with the original LeNet-5 model and a back-propagation neural network (BPNN). The results showed that the characters after preprocessing and character segmentation were clearer than those in the original images. During training, the optimized LeNet-5 converged the fastest, reached stability after 100 iterations, and had the smallest error after stability. The overall recognition accuracy of the BPNN model for the license images was 64.3%. For the original LeNet-5 model, it was 84.0%, and for the optimized LeNet-5 model, it was 98.6%.
목차
Abstract
1. Introduction
2. License Plate Recognition based on Optimized LeNet-5
3. Simulation Experiments
4. Experimental Results
5. Discussion
6. Conclusion
References
참고문헌 (20)
참고문헌 신청
W. Huang, X. Xu, M. Hu, and W. Huang, “A license plate recognition data to estimate and visualise the restriction policy for diesel vehicles on urban air quality: A case study of Shenzhen,” Journal of Cleaner Production, Vol. 338, pp. 1-16, 2022. Article
Y. Yu, Y. Cui, J. Zeng, C. He, and D. Wang, “Identifying traffic clusters in urban networks based on graph theory using license plate recognition data,” Physica A: Statistical Mechanics and its Applications, Vol. 591, pp. 1-16, 2022. Article (CrossRef Lin
Q. Lin, J. Chen, G. Li, and Z. He, “Signal timing parameters inference method at intersections using license plate recognition data,” IET Intelligent Transport Systems, Vol. 16, No. 8, pp. 1092-1107, 2022. Article (CrossRef Link)
Y. Wang, Z. P. Bian, and Y. Zhou, “Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach,” IEEE Transactions on Intelligent Transportation Systems, Vol. 2022, No. 7, pp. 23, 2022. Article (CrossRef Link)
T. Vetriselvi, E. L. Lydia, S. N. Mohanty, E. Alabdulkreem, S. Al-Otaibi, A. Al-Rasheed, and R. F. Mansour, “Deep Learning Based License Plate Number Recognition for Smart Cities,” Computers, Materials, and Continuum (in English), Vol. 000, No. 001, pp. 2
Y. Yu, Y. Cui, J. Zeng, C. He, and D. Wang, “Identifying traffic clusters in urban networks based on graph theory using license plate recognition data,” Physica A: Statistical Mechanics and its Applications, Vol. 591, pp. 1-16, 2022. Article (CrossRef Lin
Q. Lin, J. Chen, G. Li, and Z. He, “Signal timing parameters inference method at intersections using license plate recognition data,” IET Intelligent Transport Systems, Vol. 16, No. 8, pp. 1092-1107, 2022. Article (CrossRef Link)
Y. Wang, Z. P. Bian, and Y. Zhou, “Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach,” IEEE Transactions on Intelligent Transportation Systems, Vol. 2022, No. 7, pp. 23, 2022. Article (CrossRef Link)
T. Vetriselvi, E. L. Lydia, S. N. Mohanty, E. Alabdulkreem, S. Al-Otaibi, A. Al-Rasheed, and R. F. Mansour, “Deep Learning Based License Plate Number Recognition for Smart Cities,” Computers, Materials, and Continuum (in English), Vol. 000, No. 001, pp. 2
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