Integrating GAN-Based Image Enhancement with YOLOv5 Object Detection for Accurate Vehicle Number Plate Analysis

Main Article Content

K Venkatesh Sharma
Mustafa Abdulkareem Salman Al-Nuaimi


In this research, we propose a novel approach for the analysis of vehicle number plates, crucial for applications ranging from transportation management to law enforcement. Our methodology leverages the power of Generative Adversarial Networks (GANs) and YOLOv5, a state-of-the-art object detection model. We start by curating a diverse dataset of vehicle number plate images, encompassing variations in lighting, orientation, and image quality. We employ GANs to preprocess the dataset, enhancing image quality, reducing noise, and expanding dataset size, thus ensuring robust performance under real-world conditions. Subsequently, YOLOv5 is employed for rapid and accurate object detection within the preprocessed images, forming the core of our analysis pipeline. Through comprehensive experiments on various datasets and real-world scenarios, our approach consistently outperforms traditional methods in vehicle number plate detection and recognition tasks, demonstrating its adaptability to varying environmental conditions, including day and night settings. This research contributes significantly to the automated license plate recognition (ALPR) and image analysis field by introducing an efficient system that combines GANs' generative capabilities with YOLOv5's precision, promising advancements in transportation management, security surveillance, and law enforcement applications.

Article Details

How to Cite
M.Bhavsingh, K Venkatesh Sharma, and Mustafa Abdulkareem Salman Al-Nuaimi, “Integrating GAN-Based Image Enhancement with YOLOv5 Object Detection for Accurate Vehicle Number Plate Analysis”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 6, pp. 9–14, Jun. 2023.
Research Articles


Du, S., Ibrahim, M., Shehata, M., & Badawy, W. (2012). Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol., 23(2), 311-325.

Anagnostopoulos, C. N. E., Anagnostopoulos, I. E., Loumos, V., & Kayafas, E. (2006). A license plate-recognition algorithm considering intelligent transportation system applications. IEEE Trans. Intell. Transp. Syst., 7(3), 377-392.

Zhou, W., Li, H., Lu, Y., & Tian, Q. (2012). Principal visual word discovery considering automatic license plate detection. IEEE Trans. Image Process., 21(9), 4269-4279.

Zhang, H., Jia, W., He, X., & Wu, Q. (2006). Learning-based license plate detection using global & local features. In Proc. 18th Int. Conf. Pattern Recognit. (ICPR) (Vol. 2, pp. 1102-1105).

Wang, S.-Z., & Lee, H.-J. (2003). Detection & recognition about license plate characters among different appearances. In Proc. IEEE Intell. Transp. Syst. (Vol. 2, pp. 979-984).

Hsu, G.-S., Chen, J.-C., & Chung, Y.-Z. (2009). Application-oriented license plate recognition. IEEE Trans. Veh. Technol., 58(7), 3781-3785.

Yuan, Y. L., Zou, W. B., Zhao, Y., Wang, X., Hu, X. F., & Komodakis, N. (2016). A robust & efficient approach towards license plate detection. IEEE Trans. Image Process., 26(3), 1102-1114.

Deb, K., Gubarev, V. V., & Jo, K.-H. (2009). Vehicle license plate detection algorithm based on color space & geometrical properties. In Proc. Int. Conf. on Intell. Comput (pp. 555-564).

Chen, Z. X., Liu, C. Y., Chang, F. L., & Wang, G. Y. (2009). Automatic license-plate location & recognition based on feature salience. IEEE Trans. Veh. Technol., 58(7), 3781-3785.

Arivazhagan, S., & Ganesan, L. (2003). Texture classification using wavelet transform. Pattern Recognit. Lett., 24(9), 1513-1521.

Heikkila, M., & Pietikäinen, M. (2006). A texture-based method considering modeling background & detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell., 28(4), 657-662.

Saadouli, G., Elburdani, M. I., Al-Qatouni, R. M., Kunhoth, S., & Al-Maadeed, S. (2020). Automatic & secure electronic gate system using fusion about license plate car make recognition & face detection. In Proc. IEEE Int. Conf. Informat. IoT Enabling Technol. (ICIoT) (pp. 79-84).

Sivakumar, S. A., John, T. J., Selvi, G. T., Madhu, B., Shankar, C. U., & Arjun, K. P. (2021). IoT based Intelligent Attendance Monitoring with Face Recognition Scheme. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 349-353). IEEE.

Kim, K. K., Kim, K. I., Kim, J., & Kim, H. J. (2000). Learning-based approach considering license plate recognition. In Proc. Neural Netw. Signal Process. IEEE Signal Process. Soc. Workshop (Vol. 2, pp. 614-623).

Madhu, Bhukya, M. Venu Gopala Chari, Ramdas Vankdothu, Arun Kumar Silivery, & Veerender Aerranagula. (2023). Intrusion detection models for IOT networks via deep learning approaches. Measurement: Sensors, 25, 100641.

Rahman, C. A., Badawy, W., & Radmanesh, A. (2021). A real-time vehicle’s license plate recognition system. In Proc. IEEE Conf. Adv. Video Signal Based Surveill. (pp. 163-166).

Madhu, Bhukya, & Gopalachari, M. Venu. (2023). Classification of the Severity of Attacks on Internet of Things Networks. In Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022 (pp. 411-424). Singapore: Springer Nature Singapore.

Czajkowska, J., Rudzki, M., & Czajkowski, Z. (2008). A new fuzzy support vectors machine considering biomedical data classification. In Proc. 30th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (pp. 4676-4679).

Phung, S. L., Bouzerdoum, A., Chai, D., & Watson, A. (2004). Naive Bayes face-nonface classifier: A study about preprocessing & feature extraction techniques. In Proc. Int. Conf. Image Process. (ICIP) (Vol. 2, pp. 1385-1388).

Huang, Y.-P., Chen, C.-H., Chang, Y.-T., & Sandnes, F. E. (2009). An intelligent strategy considering checking annual inspection status about motorcycles based on license plate recognition. Expert Syst. Appl., 36(5), 9260-9267.

Yu, M., & Kim, Y. D. (2000). An approach towards Korean license plate recognition based on vertical edge matching. In Proc. SMC Conf. IEEE Int. Conf. Syst. Man Cybern. Cybern. Evolving Syst. Hum. Organizations Complex Interact. (pp. 2975-2980).

Ibrahim, H., Elattar, M. A., & Badawy, W. (2021). On application about real-time deep neural network considering automatic license plate reading from sequence about images targeting edge artificial intelligence architectures. In Enabling Machine Learning Applications in Data Science, Springer (pp. 299-311).

Silva, S. M., & Jung, C. R. (2020). Real-time license plate detection & recognition using deep convolutional neural networks. J. Vis. Commun. Image Represent., 71.

Most read articles by the same author(s)