• Horticultural Science & Technology
• /
• v.28 no.3
• /
• pp.423-428
• /
• 2010

The quality change of musk melons, divided into ripened (90 days) and over-ripened (92 days) set by the formal day maturing melons, was investigated during marketing period at both 10 and $25^{\circ}C$. The rate of weight loss was increased in all samples as the storage period passed and greater in ripened melons than over-ripened melon. The hardness decreased in both well and over-ripened melon as the storage period passed. Furthermore, changes in hardness were prevented in fruit stored at $10^{\circ}C$ compared to fruit stored at $25^{\circ}C$. Immediately after harvest, the solid solubility of over-ripened melon was 14.6%, while that of ripened fruit was 12.8%. The respiration rate of both well and over-ripened melon increased temporarily when stored at $25^{\circ}C$, which is characteristic of climacteric fruits during the first day of storage; however, no change in respiration rate was observed in fruit stored at $10^{\circ}C$. When sensory evaluation was conducted, there were no differences observed in flavor and taste among samples. However, with the exception of over-ripened melon, the texture of all samples increased significantly with storage time when melon was stored at $25^{\circ}C$. The score of overall acceptability remained high for 12 days in both well and over-ripened melon, while that of ripened melon stored at $10^{\circ}C$ and over-ripened melon stored $25^{\circ}C$ remained high for 7 and 5 days, respectively (p<0.05).

• Journal of Broadcast Engineering
• /
• v.26 no.4
• /
• pp.453-462
• /
• 2021

A lot of license plate data is required for car number recognition. License plate data needs to be balanced from past license plates to the latest license plates. However, it is difficult to obtain data from the actual past license plate to the latest ones. In order to solve this problem, a license plate recognition study through deep learning is being conducted by creating a synthetic license plates. Since the synthetic data have differences from real data, and various data augmentation techniques are used to solve these problems. Existing data augmentation simply used methods such as brightness, rotation, affine transformation, blur, and noise. In this paper, we apply a style transformation method that transforms synthetic data into real-world data styles with data augmentation methods. In addition, real license plate data are noisy when it is captured from a distance and under the dark environment. If we simply recognize characters with input data, chances of misrecognition are high. To improve character recognition, in this paper, we applied the DeblurGANv2 method as a quality improvement method for character recognition, increasing the accuracy of license plate recognition. The method of deep learning for license plate detection and license plate number recognition used YOLO-V5. To determine the performance of the synthetic license plate data, we construct a test set by collecting our own secured license plates. License plate detection without style conversion recorded 0.614 mAP. As a result of applying the style transformation, we confirm that the license plate detection performance was improved by recording 0.679mAP. In addition, the successul detection rate without image enhancement was 0.872, and the detection rate was 0.915 after image enhancement, confirming that the performance improved.