Object recognition and inspection in difficult industrial environments

被引:0
|
作者
Dominguez, Enrique [1 ]
Spinola, Carlos [2 ]
Luque, Rafael M. [2 ]
Palomo, Esteban J. [2 ]
Munoz, Jose [1 ]
机构
[1] Univ Malaga, Dept Comp Sci, ETSI Infomat, E-29071 Malaga, Spain
[2] Univ Malaga, Dept Elect, E-29071 Malaga, Spain
关键词
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The manufacturing environment is characterized by rapid change, originating new challenges and problems to the production and operation manager in the industry. In response to the need for fast and flexible manufacturing, increasing attention is being given to integration of computing technologies with the manufacturing systems leading to the development of fast and flexible manufacturing systems aided with high performance vision capabilities. This paper presents an inspection and recognition system for manufacturing applications in difficult industrial environments. In such difficult environments, where objects to be recognized can be dirty and illumination conditions cannot be sufficiently controlled, the required accuracy and rigidity of the system are critical features. The purpose of the real-time system is to inspect and recognize air-conditioning objects for avoiding deficiency in the manufactured process and erroneous identifications due to a large variety of size and kinds of objects. The proposed system is composed by five USB cameras (640x480 pixel resolution, 30 Hz frame rates and 8mm lenses) plug into a conventional computer (Pentium IV 3 GHz). The edged images are features which are relatively robust against illumination changes, so the system can work well in difficult environments mentioned. Experimental results on inspection and recognition of a large variety of air-conditioning objects are provided to show the performance of the system in a difficult environment.
引用
收藏
页码:2035 / +
页数:2
相关论文
共 50 条
  • [1] Recognition of large work objects in difficult industrial environments
    Sumi, Y
    Sallinen, M
    Sirviö, M
    Väinölä, J
    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7, 2004, : 5285 - 5289
  • [2] 3D Object Recognition via Multi-View Inspection in Unknown Environments
    Westell, Jamie
    Saeedi, Parvaneh
    11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2010), 2010, : 2088 - 2095
  • [3] Microcomputer Boards for Difficult Industrial Environments.
    Breton, Jean-Michel
    Electronique industrielle, 1984, (67): : 39 - 42
  • [4] Object Recognition for Industrial Image
    Licev, Lacezar
    Tomecek, Jan
    Babiuch, Marek
    2014 15TH INTERNATIONAL CARPATHIAN CONTROL CONFERENCE (ICCC), 2014, : 320 - 325
  • [5] Object recognition in industrial environments using support vector machines and artificial neural networks
    Barry, Timothy John
    Nagarajah, C. Romesh
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2010, 48 (5-8): : 815 - 821
  • [6] Object recognition in industrial environments using support vector machines and artificial neural networks
    Timothy John Barry
    C. Romesh Nagarajah
    The International Journal of Advanced Manufacturing Technology, 2010, 48 : 815 - 821
  • [7] Scene Recognition for Robot Localization in Difficult Environments
    Santos-Saavedra, D.
    Canedo-Rodriguez, A.
    Pardo, X. M.
    Iglesias, R.
    Regueiro, C. V.
    BIOINSPIRED COMPUTATION IN ARTIFICIAL SYSTEMS, PT II, 2015, 9108 : 193 - 202
  • [8] Robust Object Recognition in Unstructured Environments
    Martinez-Martin, Ester
    del Pobil, Angel P.
    INTELLIGENT AUTONOMOUS SYSTEMS 12, VOL 1, 2013, 193 : 705 - +
  • [9] Looking as if you know: Systematic object inspection precedes object recognition
    Holm, Linus
    Eriksson, Johan
    Andersson, Linus
    JOURNAL OF VISION, 2008, 8 (04):
  • [10] An efficient object recognition scheme for a prototype component inspection
    SarkodieGyan, T
    Lam, CW
    Hong, D
    Campbell, AW
    MECHATRONICS, 1997, 7 (02) : 185 - 197