Model for identification of correct positioning of parts in a pick and place system

Author:

Campas-Buitimea Juan Julio Cesar1ORCID,González-López Samuel1ORCID,Medina-Muñoz Luis Arturo1ORCID,Rodriguez-Espinoza Indelfonso1ORCID

Affiliation:

1. Instituto Tecnológico de Nogales

Abstract

This article investigates the use of automatic learning classification techniques applied to the task of recognizing the correct shape and color of pieces in a connector using neural networks. The system presented here shows that you can use a set of features extracted from the side view of the piece to recognize the shape of the piece and the color. The proposed model is based on two stages, one performs detection and the other is for recognition. In the first stage, color segmentation algorithms have been tested. In the second stage, a method of extracting personalized features in a color recognition approach is used. Finally, the use of a multilayer artificial neural network (ANN) is proposed to recognize and interpret the different possible shapes and colors with which the pieces can come.

Publisher

ECORFAN

Reference2 articles.

1. De La Escalera, A.; Moreno, L.E.; Salichs, M.A.; Armingol, J.M. Road traffic sign detection and classification. IEEE Trans. Ind. Electr. 1997, 44, 848–859. [CrossRef]

2. Deshmukh, V.R.; Patnaik, G.; Patil, M. Real-time traffic sign recognition system based on colour image segmentation. Int. J. Comput. Appl. 2013, 83. [CrossRef]Mariut, F.; Fosalau, C.; Avila, M.; Petrisor, D. Detection and recognition of traffic signs using gabor filters. In Proceedings of the 2011 34th International Conference on Telecommunications and Signal Processing (Tsp), Budapest, Hungary, 18–20 August 2011; pp. 554–558.Rizvi, R.; Kalra, S.; Gosalia, C.; Rahnamayan, S. Fuzzy adaptive cruise control system with speed sign detection capability. In Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, BeijingShoba, E.; Suruliandi, A. Performance analysis on road sign detection, extraction and recognition techniques. In Proceedings of the 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), Nagercoil, India, 20–21 March 2013; pp. 1167–1173.Wu, J.; Si, M.; Tan, F.; Gu, C. Real-time automatic road sign detection. In Proceedings of the Fifth International Conference on Image and Graphics (ICIG’09), Xi’an, China, 20–23 September 2009; pp. 540–544.Y. Nguwi and A. Kouzani, “Automatic road sign recognition using neural networks,” in Proc. Int. Joint Conf. Neural Netw., Vancouver, BC, Canada, 2006, pp. 3955–3962. Yamamoto, J.; Karungaru, S.; Terada, K. Japanese road signs recognition using neural networks. In Proceedings of the SICE Annual Conference, Nagoya, Japan, 14–17 September 2013; pp. 1144–1150.Zhang, Q.S.; Kamata, S. Improved color barycenter model and its separation for road sign detection. IEICE Trans. Inf. Syst. 2013, E96D, 2839–2849. [CrossRef]

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3