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Broiler stunned state detection based on an improved fast region-based convolutional neural network algorithm.

Chang-Wen Ye1, Khurram Yousaf1, Chao Qi1

  • 1College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.

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|May 18, 2020
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Summary
This summary is machine-generated.

A new Faster-RCNN+MRMnet algorithm accurately identifies broiler stunning levels, achieving 98.06% accuracy. This automated system inspects over 40,000 birds hourly, enhancing poultry processing efficiency and reducing costs.

Keywords:
broilerconvolutional neural networkdeep learningelectrical stunningstunned state detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Poultry Science

Background:

  • Accurate assessment of broiler stunning is crucial for animal welfare and meat quality.
  • Current methods for assessing stunning may be subjective or labor-intensive.
  • Automated systems are needed to improve efficiency and consistency in poultry processing.

Purpose of the Study:

  • To develop and evaluate an improved fast region-based convolutional neural network (RCNN) algorithm for recognizing broiler stunning states.
  • To enhance the accuracy and efficiency of automated broiler stunning detection.
  • To provide a reliable tool for online inspection in poultry processing plants.

Main Methods:

  • Collected image samples of stunned broilers from a slaughter line.
  • Created a PASCAL VOC-formatted dataset with annotated broiler head and wing areas.
  • Utilized rotation and flip data augmentation to improve dataset effectiveness.
  • Developed a Faster-RCNN+MRMnet model incorporating a multi-layer residual module (MRM) for detailed feature extraction.

Main Results:

  • The Faster-RCNN+MRMnet model achieved an identification accuracy of 98.06% on a dataset of 27,828 images.
  • Outperformed a backpropagation neural network (90.11%) and a standard Faster-RCNN model (96.86%).
  • The algorithm can inspect over 40,000 broilers per hour, demonstrating high processing speed.

Conclusions:

  • The proposed Faster-RCNN+MRMnet algorithm offers a significant improvement in accuracy and efficiency for detecting broiler stunning states.
  • This automated approach can increase operational efficiency, reduce labor and costs in poultry processing.
  • The system holds potential for widespread adoption in online inspection applications within the poultry industry.