Evaluating convolutional neural networks for cage-free floor egg detection

Abstract

The manual collection of eggs laid on the floor (or ‘floor eggs’) in cage-free (CF) laying hen housing is strenuous and time-consuming. Using robots for automatic floor egg collection offers a novel solution to reduce labor yet relies on robust egg detection systems. This study sought to develop vision-based floor-egg detectors using three Convolutional Neural Networks (CNNs), i.e., single shot detector (SSD), faster region-based CNN (faster R-CNN), and region-based fully convolutional network (R-FCN), and evaluate their performance on floor egg detection under simulated CF environments. The results show that the SSD detector had the highest precision (99.9 ± 0.1%) and fastest processing speed (125.1 ± 2.7 ms·image−1) but the lowest recall (72.1 ± 7.2%) and accuracy (72.0 ± 7.2%) among the three floor-egg detectors. The R-FCN detector had the slowest processing speed (243.2 ± 1.0 ms·image−1) and the lowest precision (93.3 ± 2.4%). The faster R-CNN detector had the best performance in floor egg detection with the highest recall (98.4 ± 0.4%) and accuracy (98.1 ± 0.3%), and a medium prevision (99.7 ± 0.2%) and image processing speed (201.5 ± 2.3 ms·image−1); thus, the faster R-CNN detector was selected as the optimal model. The faster R-CNN detector performed almost perfectly for floor egg detection under a wide range of simulated CF environments and system settings, except for brown egg detection at 1 lux light intensity. When tested under random settings, the faster R-CNN detector had 91.9–94.7% precision, 99.8–100.0% recall, and 91.9–94.5% accuracy for floor egg detection. It is concluded that a properly-trained CNN floor-egg detector may accurately detect floor eggs under CF housing environments and has the potential to serve as a crucial vision-based component for robotic floor egg collection systems.