Classify broiler viscera using an iterative approach on noisy labeled training data

Abstract

Poultry meat is produced and slaughtered at higher and higher rates and the manual food safety inspection is now becoming the bottleneck. An automatic computer vision system could not only increase the slaughter rates but also lead to a more consistent inspection. This paper presents a method for classifying broiler viscera into healthy and unhealthy, in a data set recorded in-line at a poultry processing plant. The results of the on-site manual inspection are used to automatically label the images during the recording. The data set consists of 36,228 images of viscera. The produced labels are noisy, so the labels in the training set are corrected through an iterative approach and ultimately used to train a convolutional neural network. The trained model is tested on a ground truth data set labelled by experts in the field. A classification accuracy of 86% was achieved on a data set with a large in-class variation.