Super-Resolved Recognition of License Plate Characters

Sung-Jin Lee and Seok Bong Yoo
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Sung-Jin Lee: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 11866, Korea
Seok Bong Yoo: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 11866, Korea

Mathematics, 2021, vol. 9, issue 19, 1-19

Abstract: Object detection and recognition are crucial in the field of computer vision and are an active area of research. However, in actual object recognition processes, recognition accuracy is often degraded due to resolution mismatches between training and test image data. To solve this problem, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique that improves object recognition accuracy. In detail, we collected a number of license plate training images through web-crawling and artificial data generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to image flips. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on representative test images and confirmed that the proposed super-resolution technique improves the accuracy of character recognition. For character recognition with the 4× magnification, the proposed method remarkably increased the mean average precision by 49.94% compared to the existing state-of-the-art method.

Keywords: super-resolved recognition; license plate characters; data augmentation; flip loss function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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