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Article type: Research Article
Authors: Ganesh, M.A.a | Saravana Perumaal, S.b; * | Gomathi Sankar, S.M.a
Affiliations: [a] Department of Mechatronics Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India | [b] Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chennai, Tamil Nadu, India
Correspondence: [*] Corresponding author. S. Saravana Perumaal, Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chennai, Tamil Nadu, India. E-mail: [email protected].
Abstract: The current framework for detecting Fake License Plates (FLP) in real-time is not robust enough for patrol teams. The objective of this paper is to develop a robust license plate authentication framework, based on the Vehicle Make and Model Recognition (VMMR) and the License Plate Recognition (LPR) algorithms that is implementable at the edge devices. The contributions of this paper are (i) Development of license plate database for 547 Indian cars, (ii) Development of an image dataset with 3173 images of 547 Indian cars in 8 classes, (iii) Development of an ensemble model to recognize vehicle make and model from frontal, rear, and side images, and (iv) Development of a framework to authenticate the license plates with frontal, rear, and side images. The proposed ensemble model is compared with the state-of-the-art networks from the literature. Among the implemented networks for VMMR, the Ensembling model with a size of 303.2 MB achieves the best accuracy of 89% . Due to the limited memory size, Easy OCR is chosen to recognize license plate. The total size of the authentication framework is 308 MB. The performance of the proposed framework is compared with the literature. According to the results, the proposed framework enhances FLP recognition due to the recognition of vehicles from side images. The dataset is made public at https://www.kaggle.com/ganeshmailecture/datasets.
Keywords: Vehicle make and model recognition, fake license plate detection, license plate authentication, intelligent transportation system
DOI: 10.3233/JIFS-230607
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024
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