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Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet
Computational Intelligence and Neuroscience Pub Date : 2021-03-27 , DOI: 10.1155/2021/5544784
Sun-Kuk Noh 1
Affiliation  

Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short-term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers’ health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer-learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know-how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.

中文翻译:

使用智能物联网和AlexNet深度学习的可回收服装分类系统

最近,以机器学习和深度学习为主导的物联网(IoT)和人工智能(AI)已经成为第四次工业革命(4IR)的关键技术。特别是,使用深度学习的对象识别技术目前正在各个领域中使用,并且由于深度学习的强大性能和潜力,许多研究小组和信息技术(IT)公司目前都在深度学习上进行了大量投资。纺织工业在所有过程中都涉及大量的人力资源,例如原材料的收集,染色,加工和缝纫,而资源和能源的浪费以及对环境的污染的增加是由于在生产过程中所产生的衣服的短期浪费而造成的。这些过程。通过使用再生衣服可以大大减少环境污染。在韩国,再生服装的利用率不断提高,旧服装的使用量很高,年消费额为562亿美元,但由于采用了手工回收服装收集系统,因此使用不当。它存在一些问题,例如封闭的工作环境,工人的健康,劳动力成本上升以及处理速度慢,这使得在将回收的服装运输到工厂时很难应用现有的服装识别技术,该技术按服装的变形和重叠形状进行分类。输送带。在这项研究中,我提出了一种使用物联网和人工智能的可回收服装分类系统,该系统使用对象识别技术解决了这些问题。物联网设备由Raspberry pi和摄像头组成,AI使用通过迁移学习的AlexNet对不同类型的衣服进行分类。这项研究的结果证实,可以预测使用人工智能的再生服装的类型,并且可以执行准确的分类工作,而不是服装分类工作现场的工人的经验和专门知识。空间。这将引导由现有工作人员中的人员执行的回收服装分类工作的创新方向。换句话说,期望将实现必要过程的标准化,人工智能的利用,自动化系统的应用,各种成本降低和工作效率的提高。这项研究的结果证实,可以预测使用人工智能的再生服装的类型,并且可以执行准确的分类工作,而不是服装分类工作现场的工人的经验和专门知识。空间。这将引导由现有工作人员中的人员执行的回收服装分类工作的创新方向。换句话说,期望将实现必要过程的标准化,人工智能的利用,自动化系统的应用,各种成本降低和工作效率的提高。这项研究的结果证实,可以预测使用人工智能的再生服装的类型,并且可以执行准确的分类工作,而不是服装分类工作现场的工人的经验和专门知识。空间。这将引导由现有工作人员中的人员执行的回收服装分类工作的创新方向。换句话说,期望将实现必要过程的标准化,人工智能的利用,自动化系统的应用,各种成本降低和工作效率的提高。已经证实,可以预测使用人工智能的再生服装的类型,并且可以进行精确的分类工作,而不是在封闭空间的服装分类工作现场中的工作人员的经验和专门知识。这将引导由现有工作人员中的人员执行的回收服装分类工作的创新方向。换句话说,期望将实现必要过程的标准化,人工智能的利用,自动化系统的应用,各种成本降低和工作效率的提高。已经证实,可以预测使用人工智能的再生服装的类型,并且可以进行精确的分类工作,而不是在封闭空间的服装分类工作现场中的工作人员的经验和专门知识。这将引导由现有工作人员中的人员执行的回收服装分类工作的创新方向。换句话说,期望将实现必要过程的标准化,人工智能的利用,自动化系统的应用,各种成本降低和工作效率的提高。这将引导由现有工作人员中的人员执行的回收服装分类工作的创新方向。换句话说,期望将实现必要过程的标准化,人工智能的利用,自动化系统的应用,各种成本降低和工作效率的提高。这将引导由现有工作人员中的人员执行的回收服装分类工作的创新方向。换句话说,期望将实现必要过程的标准化,人工智能的利用,自动化系统的应用,各种成本降低和工作效率的提高。
更新日期:2021-03-27
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