Digital Image Processing

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Automatic Parking System using Vehicle License Plate Detection


Affiliations
1 Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, India
2 Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, India
     

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With phenomenal increase in the number of vehicles, vehicular systems and parking systems are a major challenge faced by urban cities. Parking systems currently rely on manual labour for noting down the registration numbers of vehicles entering the parking system. Our project aims at developing a parking system what would detect a vehicle while entering in the parking lot, and also would automatically recognize its registration number at day and night time. Parking lots are generally closed structures so there is always low light and at night time there are extremely low light conditions. Any camera would fail to provide noise free image at this condition. So there is need for low light enhancement. This paper discusses some of the existing low image enhancement algorithms. From the enhanced image, computation is done in detecting the vehicle, classifying it into4 wheelers or 2 wheeler vehicles and also extracting the registration number from the detected license plate. This paper deals with several existing architectures and models which perform ALPR on benchmark dataset.

Keywords

ALPR, Low Light Enhancement, License Plate Detection, Character Recognition.
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  • V. Janani, M. Dinakaran: Image Enhancement Techniques- A Review. 2nd International Conference on Current Trends in Engineering and Technology, ICCTET’14. IEEE Conference Number - 33344. July 8, 2014, Coimbatore, India.

  • Abdullah-Al-Wadud, M., Kabir, M., Akber Dewan, M., and Chae, O. (2007): A Dynamic Histogram Equalization for Image Contrast Enhancement. IEEE Transactions on Consumer Electronics, 53(2), 593– 600. doi:10.1109/tce.2007.381734

  • Boyat, Ajay Kumar, and Brijendra Kumar Joshi: A review paper: noise models in digital image processing. ArXiv preprint arXiv: 1505.03489 (2015).

  • Baozhong, L. I. U., and L. I. U. Jianbin: Overview of image noise reduction based on non-local mean algorithm. MATEC Web of Conferences. Vol. 232. EDP Sciences, 2018.

  • Wei, Ji, et al: A night-time image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot. International Journal of Advanced Robotic Systems 15.1 (2018): 1729881417753871.

  • L. Li, R. Wang, W. Wang, and W. GAO. A low-light image enhancement method for both denoising and contrast enlarging. In Proceedings of IEEE Conference on Image Processing (ICIP), pages 3730–3734, 2015.

  • G. Hsu, J. Chen, Y. Chung, Application-oriented license plate recognition, IEEE Trans. Veh. Technol. 62 (2) (2013) 552–561.

  • H. Li, P. Wang, M. You, C. Shen, Reading car license plates using deep neural networks, Image and Vision Computing 72 (2018) 14–23.

  • Jamtsho, Yonten, Panomkhawn Riyamongkol, and Rattapoom Waranusast: Real-time Bhutanese license plate localization using YOLO. ICT Express 6.2 (2020): 121-124.

  • Laroca, Rayson, and David Menotti. "Automatic License Plate Recognition: An Efficient and Layout-Independent Approach Based on the YOLO Detector." Anais do XXXIII Concurso de Teses e Dissertações. SBC, 2020.

  • Pustokhina, Irina Valeryevna, et al. "Automatic Vehicle License Plate Recognition using Optimal K-Means with Convolutional Neural Network for Intelligent Transportation Systems." IEEE Access (2020).

  • Lu, Qiang, et al. "License plate detection and recognition using hierarchical feature layers from CNN." Multimedia Tools and Applications 78.11 (2019): 15665-15680.

  • Vasek, Vojtech, Vojtech Franc, and Martin Urban. "License Plate Recognition and Super-resolution from Low-Resolution Videos by Convolutional Neural Networks." BMVC. 2018.

  • Sarfraz, M. Saquib, et al. "Real-time automatic license plate recognition for CCTV forensic applications." Journal of real-time image processing 8.3 (2013): 285-295.

  • Yoo, Seok Bong, and Mikyong Han: Temporal matching prior network for vehicle license plate detection and recognition in videos. ETRI Journal (2020).

  • Gonçalves, Gabriel Resende, et al. "Real-time automatic license plate recognition through deep multi-task networks." 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2018.

  • Cheang, Teik Koon, Yong Shean Chong, and Yong Haur Tay. "Segmentation-free vehicle license plate recognition using ConvNetRNN." arXiv preprint arXiv:1701.06439 (2017).

  • Kim, S. G., H. G. Jeon, and H. I. Koo. "Deep-learning-based license plate detection method using vehicle region extraction." Electronics Letters 53.15 (2017): 1034-1036.

  • Yilmaz, Abdullah Asim, et al. "A vehicle detection approach using deep learning methodologies." arXiv preprint arXiv:1804.00429 (2018).

  • S. M. Silva and C. R. Jung, “Real-time brazilian license plate detection and recognition using deep convolutional neural networks,” in Conference on Graphics, Patterns and Images (SIBGRAPI), 2017, pp. 55–62.

  • Szegedy, Christian, et al. "Inception-v4, inception-resnet and the impact of residual connections on learning: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 31. No. 1. 2017.


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  • Automatic Parking System using Vehicle License Plate Detection

Abstract Views: 156  |  PDF Views: 0

Authors

P. Kushwanth
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, India
R. Monisha
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, India
Sindhura Bharadwaj
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, India
A. Abhinav
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, India
K. N. Sowmya
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, India

Abstract


With phenomenal increase in the number of vehicles, vehicular systems and parking systems are a major challenge faced by urban cities. Parking systems currently rely on manual labour for noting down the registration numbers of vehicles entering the parking system. Our project aims at developing a parking system what would detect a vehicle while entering in the parking lot, and also would automatically recognize its registration number at day and night time. Parking lots are generally closed structures so there is always low light and at night time there are extremely low light conditions. Any camera would fail to provide noise free image at this condition. So there is need for low light enhancement. This paper discusses some of the existing low image enhancement algorithms. From the enhanced image, computation is done in detecting the vehicle, classifying it into4 wheelers or 2 wheeler vehicles and also extracting the registration number from the detected license plate. This paper deals with several existing architectures and models which perform ALPR on benchmark dataset.

Keywords


ALPR, Low Light Enhancement, License Plate Detection, Character Recognition.

References