Innovative Automatic detection of vehicle number plates using machine learning algorithms and improving the accuracy of recognition. Two sample groups using 237 images forms the sample dataset, which is tested at 80% for G power with t-test analysis. To improve the accuracy of recognition, the bernsen algorithm is proposed and compared with the genetic algorithm. Test results prove that in an uneven illuminated environment the bernsen algorithm has an accuracy of 91.5 %, which seems to be better than the genetic algorithm’s accuracy of 88.9%. Since the significance is around 0.17, there is a statistically significant difference among the study group with (p<0.05). For distorted and damaged images, the detection and recognition of number plates using the bernsens method seems to appear better then the genetic algorithm. Detection of violations using road side cameras can perform better with our proposed work.
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4 May 2023
INTERNATIONAL CONFERENCE ON ENVIRONMENTAL ENGINEERING AND MATERIAL SCIENCES: ICEEMS-2021
20–21 July 2021
Coimbatore, India
Research Article|
May 04 2023
Damaged number plate detection to improve the accuracy rate using bernsen algorithm over genetic algorithm
Maheswari Petlu;
Maheswari Petlu
a
Saveetha University
No.162, Poonamallee High Rd, Velappanchavadi, Maduravoyal, Chennai, Tamil Nadu 600077, India
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Udhayakumar Shanmugam;
Udhayakumar Shanmugam
b
Saveetha University
No.162, Poonamallee High Rd, Velappanchavadi, Maduravoyal, Chennai, Tamil Nadu 600077, India
bCorresponding author: [email protected]
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Karthik Elangovan
Karthik Elangovan
Saveetha University
No.162, Poonamallee High Rd, Velappanchavadi, Maduravoyal, Chennai, Tamil Nadu 600077, India
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bCorresponding author: [email protected]
AIP Conf. Proc. 2655, 020074 (2023)
Citation
Maheswari Petlu, Udhayakumar Shanmugam, Karthik Elangovan; Damaged number plate detection to improve the accuracy rate using bernsen algorithm over genetic algorithm. AIP Conf. Proc. 4 May 2023; 2655 (1): 020074. https://doi.org/10.1063/5.0134437
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