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• W4226388527 abstract "This paper addresses the issue of automatic plate recognition in road safety systems. Practical observations show that a large number of road safety systems in production still use OCR-only approach for plate recognition tasks. Full scale experiment has been conducted by the authors with the Axis P1344 high-speed camera unit, installed on highway and an OCR-only based detection system, which confirmed instability of accuracy and detection time under volatile environmental conditions, such as lighting, camera-caused image issues, weather, mechanical issues, etc. A method and algorithm were proposed for custom data training and automated plate recognition with pre-trained convolutional neural network (CNN) based detection system Yolo v5 as an intermediate logical layer of detection. Methods for image classification, dataset formation and training automation were used with a custom dataset of 2000 environmentally variable images. The efficiency of automated plate recognition in various conditions was verified with developed software using a set of 20 000 input images with a plate detection precision value more than 98% with average detection time 0,6s. The results obtained have practical application as an opportunity to improve the plate recognition process accuracy with plate-only region detection by CNN detection system, which is especially important when detecting in volatile environmental conditions." @default.
• W4226388527 created "2022-05-05" @default.
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• W4226388527 date "2022-01-01" @default.
• W4226388527 modified "2023-09-30" @default.
• W4226388527 title "Automated License Plate Recognition Process Enhancement with Convolutional Neural Network Based Detection System to Improve the Accuracy and Reliability of Vehicle Recognition" @default.
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• W4226388527 doi "https://doi.org/10.1007/978-3-031-04812-8_21" @default.
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