Abstract

Retail food packaging contains information which
informs choice and can be vital to consumer health, including
product name, ingredients list, nutritional information, allergens,
preparation guidelines, pack weight, storage and shelf
life information (use-by / best before dates). The presence and
accuracy of such information is critical to ensure a detailed
understanding of the product and to reduce the potential for
health risks. Consequently, erroneous or illegible labeling has the
potential to be highly detrimental to consumers and many other
stakeholders in the supply chain. In this paper, a multi-source
deep learning-based domain adaptation system is proposed and
tested to identify and verify the presence and legibility of
use-by date information from food packaging photos taken
as part of the validation process as the products pass along
the food production line. This was achieved by improving the
generalization of the techniques via making use of multi-source
datasets in order to extract domain-invariant representations for
all domains and aligning distribution of all pairs of source and
target domains in a common feature space, along with the class
boundaries. The proposed system performed very well in the
conducted experiments, for automating the verification process
and reducing labeling errors that could otherwise threaten public
health and contravene legal requirements for food packaging
information and accuracy. Comprehensive experiments on our
food packaging datasets demonstrate that the proposed multisource
deep domain adaptation method significantly improves
the classification accuracy and therefore has great potential for
application and beneficial impact in food manufacturing control
systems.