@inproceedings{a348810657004c9c933f8097331259c0,
title = "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.",
keywords = "Broiler, Classification, CNN, Food safety, Viscera",
author = "Anders J{\o}rgensen and Jens Fagertun and Moeslund, {Thomas B.}",
year = "2018",
doi = "10.1007/978-3-030-03801-4_24",
language = "English",
isbn = "978-3-030-03800-7",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "264--273",
editor = "Kai Xu and Stephen Lin and Richard Boyle and Bilal Alsallakh and Matt Turek and Srikumar Ramalingam and George Bebis and Bahram Parvin and Jing Yang and Jonathan Ventura and Darko Koracin and Eduardo Cuervo",
booktitle = "Advances in Visual Computing",
address = "Germany",
note = "13th International Symposium on Visual Computing, ISVC 2018 ; Conference date: 19-11-2018 Through 21-11-2018",
}