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An approach for goose egg recognition for robot picking based on deep learning.

Y Zhang1, Y Ge1, Y Guo1

  • 1College of Mechanical Engineering of Yangzhou University, Yangzhou, China.

British Poultry Science
|January 25, 2023
PubMed
Summary

This study introduces a novel method for accurately recognizing and locating goose eggs in non-cage environments using a combination of deep learning and image processing techniques. The developed system achieves high accuracy, paving the way for intelligent robotic egg collection.

Keywords:
Intelligent pickingYOLOv5contour curve creationdeep learningegg recognition and locationfloor eggs

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Goose eggs in non-cage systems are often contaminated and discolored due to litter and feather burial.
  • Accurate recognition and location of these eggs are challenging for automated robotic picking systems.

Purpose of the Study:

  • To develop a novel method for accurate goose egg recognition and location in non-cage environments.
  • To enable intelligent robotic systems for efficient goose egg picking.

Main Methods:

  • Utilized a three-channel convolutional neural network (T-CNN) integrated with improved AlexNet, YOLOv5, egg contour curve creation, and support vector machine (SVM).
  • Employed YOLOv5 for target detection and segmentation, followed by median filtering and OTSU for image refinement.
  • Applied the Kirsch operator for edge extraction and contour fitting, with R, G, B color components fed into T-CNN for feature extraction and SVM for classification.

Main Results:

  • Achieved approximately 95.65% correct recognition of goose egg pixel blocks in segmented images.
  • Reported an incorrect recognition rate of about 3.81% with a center of mass offset of approximately 4.45 pixels.
  • Demonstrated accurate goose egg recognition and location in challenging non-cage environments.

Conclusions:

  • The proposed method effectively addresses the challenge of goose egg recognition and location in non-cage environments.
  • This approach shows significant potential for intelligent goose egg picking applications.
  • The method can be adapted for recognizing floor eggs from other species in non-cage systems.