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Evaluating Convolutional Neural Networks for Cage-Free Floor Egg Detection.

Guoming Li¹, Yan Xu², Yang Zhao¹

¹Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA.

Sensors (Basel, Switzerland)

|January 16, 2020

Summary

Robotic floor egg collection in cage-free systems is improved by a faster region-based convolutional neural network (faster R-CNN) detector. This vision-based system offers high accuracy and recall for detecting floor eggs, reducing manual labor.

Keywords:

cage-free convolutional neural network evaluation floor egg tensorflow

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Area of Science:

Agricultural Engineering
Computer Vision
Animal Science

Background:

Manual collection of floor eggs in cage-free (CF) systems is labor-intensive.
Robotic automation requires accurate egg detection systems for efficiency.

Purpose of the Study:

Develop and evaluate vision-based floor-egg detectors using Convolutional Neural Networks (CNNs).
Compare the performance of Single Shot Detector (SSD), faster region-based CNN (faster R-CNN), and Region-based Fully Convolutional Network (R-FCN).

Main Methods:

Three CNN models (SSD, faster R-CNN, R-FCN) were trained for floor egg detection.
Performance was evaluated based on precision, recall, accuracy, and processing speed in simulated CF environments.

Main Results:

Faster R-CNN achieved the highest recall (98.4%) and accuracy (98.1%), with high precision (99.7%).
SSD offered the fastest processing but lowest recall and accuracy.
R-FCN showed the slowest speed and lowest precision.

Conclusions:

The faster R-CNN model is optimal for floor egg detection in CF housing.
CNN-based detectors show potential for robotic floor egg collection systems.