To address the challenges of manual egg sorting on poultry farms, such as labor intensity and
inconsistent quality standards, Moba is developing a machine for automated egg candling
using computer vision. As part of the prototype phase, this project evaluates various methods
to process the egg images for this sorting task.
This project presents an algorithm that segments the individual eggs within the camera’s
field of view, classifies them into five categories, and tracks them across multiple images
to perform the outlet control prediction. To enhance computational efficiency, an anomaly
detector is introduced, directing classification resources exclusively to eggs with a high like-
lihood of defects.
For segmentation, the number of channels in the YOLOv8n model was significantly re-
duced, resulting in YOLOv8xn. With a 97.2% AP95, this model outperformed a classical
algorithm using domain-specific information, as well as more complex deep learning meth-
ods, such as YOLOv8n and the prompt-based FastSAM and MobileSAM models.
Various low-complexity deep learning models were evaluated and compared to classical
feature extraction combined with ensemble machine learning methods for the classification
task. Balancing accuracy and runtime, MobileNetV3 proved to be the optimal choice, achiev-
ing 95.6% accuracy on the final test set.
For anomaly detection, both classical methods and the autoencoder reconstruction loss
were evaluated. Modeling the edge count of non-defective eggs with an exponential distribu-
tion to set a threshold reduced the computational load for classification by 83%. Furthermore,
using the anomaly detector the batch size can be reduced from 32 to 8 images.
To send the eggs to the correct outlet, the predictions for each egg were combined using
thresholds on count-per-class and normalized cumulative class predictions, both without and
with anomaly detection enabled. This resulted in overall accuracies of 97.3%, 98.4%, and
98.0%, respectively.