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http://dx.doi.org/10.6109/jkiice.2021.25.6.774

Image Super-Resolution for Improving Object Recognition Accuracy  

Lee, Sung-Jin (Department of AI Convergence, Chonnam National University)
Kim, Tae-Jun (Department of SW Engineering, Chonnam National University)
Lee, Chung-Heon (Department of SW Engineering, Chonnam National University)
Yoo, Seok Bong (Department of AI Convergence, Chonnam National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.6, 2021 , pp. 774-784 More about this Journal
Abstract
The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves 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 the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.
Keywords
Image super-resolution; Object recognition; Web-crawling; Artificial-data-generation; Image flip;
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