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Retail food packaging contains information which informs choice and can bevital to consumer health, including product name, ingredients list, nutritionalinformation, allergens, preparation guidelines, pack weight, storage and shelflife information (use-by / best before dates). The presence and accuracy ofsuch information is critical to ensure a detailed understanding of the productand to reduce the potential for health risks. Consequently, erroneous orillegible labeling has the potential to be highly detrimental to consumers andmany other stakeholders in the supply chain. In this paper, a multi-source deeplearning-based domain adaptation system is proposed and tested to identify andverify the presence and legibility of use-by date information from foodpackaging photos taken as part of the validation process as the products passalong the food production line. This was achieved by improving thegeneralization of the techniques via making use of multi-source datasets inorder to extract domain-invariant representations for all domains and aligningdistribution of all pairs of source and target domains in a common featurespace, along with the class boundaries. The proposed system performed very wellin the conducted experiments, for automating the verification process andreducing labeling errors that could otherwise threaten public health andcontravene legal requirements for food packaging information and accuracy.Comprehensive experiments on our food packaging datasets demonstrate that theproposed multi-source deep domain adaptation method significantly improves theclassification accuracy and therefore has great potential for application andbeneficial impact in food manufacturing control systems.

Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging