Effects of ESRGAN in Sugar Apple Ripeness Detection

Rhys B. Sanchez, Jose Angelo C. Esteves, N. Linsangan
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Abstract

In using the CNN algorithm, detecting and classifying objects will output classification errors in images throughout its usage. Presently, using SRGAN was not tested whether it has an effect on the use of CNN in determining sugar apple ripeness. The researchers used Enhanced SRGAN to improve the quality of sugar apple images taken. Images taken from sugar apples were compared to images stored in the dataset of the device using CNN, which then tells the ripeness stage of the sugar apple image. The same set of images captured is enhanced using ESRGAN to compare if there will be an effect on the results of the system using CNN. The researchers saw that better resolution and quality of the images could produce better results based on the data collected. Images without ESRGAN saw an accuracy of 84.00% and with a confidence of 49.57%. Enhanced images using ESRGAN produced more promising results with an accuracy of 92.00% and a confidence of 52.61% compared to normal images.
ESRGAN在糖苹果成熟度检测中的作用
在使用CNN算法时,检测和分类对象在整个使用过程中都会输出图像中的分类错误。目前,使用SRGAN是否会影响CNN在测定糖苹果成熟度方面的应用,还没有进行测试。研究人员使用增强型SRGAN来提高拍摄的苹果图像的质量。使用CNN将从糖苹果上拍摄的图像与存储在设备数据集中的图像进行比较,然后告诉糖苹果图像的成熟阶段。使用ESRGAN对捕获的同一组图像进行增强,以比较是否会对使用CNN的系统结果产生影响。研究人员发现,更好的图像分辨率和质量可以根据收集到的数据产生更好的结果。无ESRGAN的图像准确率为84.00%,置信度为49.57%。与正常图像相比,使用ESRGAN的增强图像产生了更有希望的结果,准确率为92.00%,置信度为52.61%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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