Noh Sun-Kuk
National Program of Excellence in Software Center, CHOSUN University, Gwangju, Republic of Korea.
Comput Intell Neurosci. 2021 Mar 26;2021:5544784. doi: 10.1155/2021/5544784. eCollection 2021.
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.
最近，以机器学习和深度学习为引领的物联网（IoT）和人工智能（AI）已成为第四次工业革命（4IR）的关键技术。特别是，利用深度学习的目标识别技术目前正在各个领域得到应用，并且由于深度学习强大的性能和潜力，许多研究团队和信息技术（IT）公司目前都在对深度学习进行大量投资。纺织行业在原材料收集、染色、加工和缝纫等所有流程中都涉及大量人力资源，并且这些流程中生产的服装的短期浪费会导致资源和能源的浪费以及环境污染的增加。通过使用回收服装，可以在很大程度上减少环境污染。在韩国，回收服装的利用率正在提高，旧衣物数量很多，年消费量达562亿美元，但由于手工回收服装收集系统，这些旧衣物没有得到妥善利用。在将回收服装输送到传送带上时，存在诸如封闭的工作场所环境、工人健康、劳动力成本上升以及处理速度低等几个问题，这使得难以应用现有的根据服装形状的变形和重叠进行分类的服装识别技术。在本研究中，针对这些问题，我提出了一种利用物联网和人工智能以及目标识别技术的回收服装分类系统。物联网设备由树莓派和摄像头组成，人工智能使用迁移学习的AlexNet对不同类型的服装进行分类。本研究的结果证实，在封闭空间的服装分类工作现场，可以预测使用人工智能的回收服装类型，并能进行准确的分类工作，取代了熟练工人的经验和技术诀窍。这将引领现有熟练工人所进行的回收服装分类工作的创新方向。换句话说，预计将实现必要流程的标准化、人工智能的利用、自动化系统的应用、各种成本的降低以及工作效率的提高。
