POULTRY EGGS DETECTION APPLICATIONS IN THE AUTOMATIC PACKAGING CONTROL PROCESS BY YOLO MODEL

  • Nguyen Huy Cong National Center for Technological Progress
  • Doan Hong Quang National Center for Technological Progress
  • Le Hong Minh National Center for Technological Progress
  • Nguyen Huy Hung National Center for Technological Progress
  • Dang Anh Duc National Center for Technological Progress
  • Nguyen Chi Long National Center for Technological Progress
  • Nguyen Huy Khan National Center for Technological Progress
  • Nguyen Tuan Hung National Center for Technological Progress
Keywords: Deep Learning, Convolutional Neural Network, YOLO Network, Eggs Detection

Abstract

Egg detection plays an important role in the poultry farming industry utilizing high technology. In the poultry farms producing eggs, the stages of automatically placing eggs into trays require extra steps to
count the number of eggs as well as to detect faulty eggs (baby eggs, broken eggs, etc.). In addition, egg inspections to ensure sufficient quantity and support reporting are necessary for trading and distributing companies. In order to address these challenges, the authors propose an improved YOLOv3 model for egg detection by processing data obtained from surveillance cameras.

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Published
2022-03-31
How to Cite
Nguyen Huy Cong, Doan Hong Quang, Le Hong Minh, Nguyen Huy Hung, Dang Anh Duc, Nguyen Chi Long, Nguyen Huy Khan, & Nguyen Tuan Hung. (2022). POULTRY EGGS DETECTION APPLICATIONS IN THE AUTOMATIC PACKAGING CONTROL PROCESS BY YOLO MODEL. UTEHY Journal of Science and Technology, 33, 41-47. Retrieved from http://jst.utehy.edu.vn/index.php/jst/article/view/520