Browse > Article
http://dx.doi.org/10.30693/SMJ.2020.9.3.41

Classification of Clothing Using Googlenet Deep Learning and IoT based on Artificial Intelligence  

Noh, Sun-Kuk (조선대학교 SW중심대학사업단)
Publication Information
Smart Media Journal / v.9, no.3, 2020 , pp. 41-45 More about this Journal
Abstract
Recently, artificial intelligence (AI) and the Internet of things (IoT), which are represented by machine learning and deep learning among IT technologies related to the Fourth Industrial Revolution, are applied to our real life in various fields through various researches. In this paper, IoT and AI using object recognition technology are applied to classify clothing. For this purpose, the image dataset was taken using webcam and raspberry pi, and GoogLeNet, a convolutional neural network artificial intelligence network, was applied to transfer the photographed image data. The clothing image dataset was classified into two categories (shirtwaist, trousers): 900 clean images, 900 loss images, and total 1800 images. The classification measurement results showed that the accuracy of the clean clothing image was about 97.78%. In conclusion, the study confirmed the applicability of other objects using artificial intelligence networks on the Internet of Things based platform through the measurement results and the supplementation of more image data in the future.
Keywords
Artificial Intelligent; Deep Learning; GoogLeNet; IoT; Raspberry Pi;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 김영근, 조민희, 김원중. "라즈베리파이 보드 기반의 빅데이터 분석을 위한 학습시스템," 한국전자통신학회, 제11권, 제4호, 433-440쪽, 2016년   DOI
2 김강철 외 1인. "라즈베리파이를 이용한 얼굴검출인식시스템 개발," 한국전자통신학회지, 제12권, 제5호, 859-8646쪽, 2017년
3 김재상, 문해민, 반성범. "오픈소스 하드웨어 기반차선검출 기술에 대한 연구," 한국스마트미디어저널, 제6권, 제3호, 15-20쪽, 2017년
4 Karl Weiss, Taghi M. Khoshgoftaar, DingDing Wang, "A survey of transfer learning," Joural of Big data, May, 2016.
5 라즈베리파이재단, www.raspberrypi.org (accessed April, 10, 2020).
6 이정륜, 이민선, 손혜연, 노순국, "라즈베리파이를 이용한 움직이는 물체 촬영 및 블루투스 전송," 2020 한국스마트미디어학회 춘계학술대회, vol. 9, no. 1, 2020년 5월
7 딥러닝을 사용하여 웹캠 영상 분류하기, https://kr.mathworks.com/help/deeplearning/examples/classify-images-from-webcam-using-deep-learning.html (accessed March, 3, 2020).
8 유병인 외 5명, "인간수준에 근접한 딥러닝 기반 영상 인식의 동향," 한국정보과학회지, 제33권, 제9호, 32-41쪽, 2015년 9월
9 Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich., "Going deeper with convolutions," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. 2015.
10 정상근, "산업의 변화와 인공지능," 한국통신학회지(정보와 통신), 제33권, 제10호, 57-59쪽, 2016년 9월
11 Dong Ju Park, Byeong Woo Kim, Young-Seon Jeong, Chang Wook Ahn, "Deep Neural Network Based Prediction of Daily Spectators for Korean Baseball League : Focused on Gwangju-KIA Champions Field," Smart Media Journal, vol. 7, no. 1, pp. 16-23, 2018.   DOI
12 Jung-Won Oh, Hangkon Kim, Il-Tae Kim, "Design and Implementation of Fruit harvest time Predicting System based on Machine Learning," Smart Media Journal, vol. 8, no. 1, pp. 73-81, 2019.
13 인공지능, 객체인식, 반드시 알아야 할 3가지, http://www.aitimes.kr/news/articleView.html?idxno=12087 (accessed March, 3, 2020).
14 문성은 외 3명, "기계학습 및 딥러닝 기술 동향," 한국통신학회지(정보와 통신), 제33권, 제10호, 49-56쪽, 2016년 9월