• DocumentCode
    2070999
  • Title

    An Automatic Dead Chicken Detection Algorithm Based on SVM in Modern Chicken Farm

  • Author

    Zhu, Weixing ; Peng, Yansong ; Bin Ji

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Jiangsu Univ., Zhenjiang, China
  • fYear
    2009
  • fDate
    26-28 Dec. 2009
  • Firstpage
    323
  • Lastpage
    326
  • Abstract
    An automatic detection algorithm for dead birds based on support vector machine (SVM) is proposed. Firstly, according to the changes of central region of cockscomb in the picture, logic and operation is used to remove the image of live chickens; Secondly, in order to distinguish accurately the dead birds in processed picture, the perimeter, area, eccentricity and complexity of the cockscomb are extracted as the variables. The changes of these variables are defined as the feature vectors. The samples of the above feature vectors are used to train SVM classifier. During the training, the grid search method is used to optimize the kernel width and punishment factor of SVM and the classifier for dead bird is designed finally. The results of experiment show that the detection accuracy is over 90%.
  • Keywords
    farming; feature extraction; image classification; learning (artificial intelligence); search problems; support vector machines; SVM classifier training; automatic dead chicken detection algorithm; cockscomb complexity; dead birds; eccentricity; feature vectors; grid search method; live chickens; modern chicken farm; picture processing; punishment factor; support vector machine; training; Birds; Data mining; Design optimization; Detection algorithms; Information science; Logic; Pattern recognition; Search methods; Support vector machine classification; Support vector machines; automatic detection; dead chicken detection; modern chicken farm; support vector machine (SVM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Engineering (ISISE), 2009 Second International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-6325-1
  • Electronic_ISBN
    978-1-4244-6326-8
  • Type

    conf

  • DOI
    10.1109/ISISE.2009.62
  • Filename
    5447214