HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images
This addresses the challenge of ensuring halal compliance in meat production for Muslims, but it is incremental as it applies existing deep learning techniques to a specific domain.
The paper tackles the problem of automatically classifying whether slaughtered chickens are halal by using a deep neural network (HalalNet) that analyzes images from a slaughterhouse conveyor, achieving high accuracy with limited data through one-shot and transfer learning.
Halal requirement in food is important for millions of Muslims worldwide especially for meat and chicken products, insuring that slaughter houses adhere to this requirement is a challenging task to do manually. In this paper a method is proposed that uses a camera that takes images of slaughtered chicken on the conveyor in a slaughter house, the images are then analyzed by a deep neural network to classify if the image is of a halal slaughtered chicken or not. However, traditional deep learning models require large amounts of data to train on, which in this case these amounts of data were challenging to collect especially the images of non-halal slaughtered chicken, hence this paper shows how the use of one shot learning [1] and transfer learning [2] can reach high accuracy on the few amounts of data that were available. The architecture used is based on the Siamese neural networks architecture which ranks the similarity between two inputs [3] while using the Xception network [4] as the twin networks. We call it HalalNet. This work was done as part of SYCUT (syriah compliant slaughtering system) which is a monitoring system that monitors the halalness of the slaughtered chicken in a slaughter house. The data used to train and validate HalalNet was collected from the Azain slaughtering site (Semenyih, Selangor, Malaysia) containing images of both halal and non-halal slaughtered chicken.