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Deep Neural Networks for Image-Based Dietary Assessment
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Research on automatic classification and detection of chicken parts based on deep learning algorithm.

Yan Chen1, Xianhui Peng1, Lu Cai1

  • 1School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, China.

Journal of Food Science
|September 1, 2023
PubMed
Summary

This study introduces a real-time chicken part classification and detection method using YOLOV4 deep learning, significantly improving poultry processing efficiency. The YOLOV4-CSPDarknet53 model achieved 98.86% mAP, outperforming other models and reducing processing time.

Keywords:
chicken partscomparison testdeep learningdetection effecttarget detection

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Area of Science:

  • Computer Vision and Machine Learning
  • Agricultural Technology
  • Food Processing Automation

Background:

  • Accurate chicken part identification is crucial for poultry processing efficiency.
  • Overlapping chicken parts pose a significant challenge to current identification systems.
  • Existing methods lack the real-time accuracy needed for high-speed assembly lines.

Purpose of the Study:

  • To develop a real-time classification and detection method for chicken parts.
  • To address the challenge of overlapping chicken parts in automated processing.
  • To enhance the productivity and reduce waste in poultry processing plants.

Main Methods:

  • A dataset of 600 chicken part images was created and augmented.
  • The YOLOV4 deep learning model (YOLOV4-CSPDarknet53) was employed for object detection.
  • Performance was evaluated using mean average precision (mAP) and inference speed, compared against YOLOV3 and SSD models.

Main Results:

  • The YOLOV4-CSPDarknet53 model achieved a 98.86% mAP with an inference speed of 22.2 ms.
  • This performance surpassed comparative models (YOLOV3, SSD) in both accuracy and speed.
  • The method successfully identified segmented chicken parts in real-time, even with occlusion.

Conclusions:

  • The proposed YOLOV4-based method offers a highly accurate and efficient solution for real-time chicken part classification and detection.
  • This technology can significantly reduce waste and improve resource utilization in poultry processing.
  • The system provides visual technical assistance for optimized sorting, cutting, and catering to consumer preferences.