Faster RCNN Algorithm for Object Detection and Thereby provides a way for Tile Grading

D.A.I.S Sewwandi, D. Vidanagama
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Abstract

Nowadays, each industry uses different kinds of smart appliances to make their day-to-day tasks easy. Because of the emergence of new technologies, one of those affected industries is the manufacturing industry which has a major concern about automating the manufacturing processes to provide quality-assured products on time. Among the production companies, tile manufacturing is facing a huge problem in the process of quality checking. Although the whole manufacturing process is automated, quality checking has a manual process. Because of the human involvement in this process, it occurs mistakes when it tries to do mass production based on the demand. So this study attempts on bringing a novel way for the existing manual tile grading mechanism using newer technology in deep learning, the object detection in the computer vision area to bring a novel outcome.
更快的RCNN算法用于目标检测,从而为瓷砖分级提供了一种方法
如今,每个行业都使用不同类型的智能电器来简化他们的日常工作。由于新技术的出现,受影响的行业之一是制造业,制造业主要关注制造过程的自动化,以及时提供有质量保证的产品。在生产企业中,瓷砖生产在质量检测过程中面临着巨大的问题。虽然整个制造过程是自动化的,但质量检查有一个人工过程。由于人类参与了这一过程,当它试图根据需求进行大规模生产时,就会出现错误。因此本研究试图为现有的人工瓷砖分级机制带来一种新颖的方式,利用较新的深度学习技术,在计算机视觉领域的目标检测方面带来一种新颖的成果。
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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