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 practice, due to the high volume of food packages that go through the supply chain, mistakes do occur therefore good quality of images are needed to verify the correctness of the information. 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 incorporating new loss functions and 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 multi-source deep domain adaptation method significantly improves the classification accuracy and therefore has great potential for application and beneficial impact in food manufacturing control systems.

零售食品包装中包含的信息可以告知消费者选择的信息，并且对消费者的健康至关重要，包括产品名称，成分清单，营养信息，过敏原，制备指南，包装重量，储存和保质期信息（有效期/有效期之前）。此类信息的存在和准确性对于确保对产品的详细了解并减少潜在的健康风险至关重要。因此，错误或难以辨认的标签可能会严重损害供应链中的消费者和许多其他利益相关者。实际上，由于经过供应链的大量食品包装会发生错误，因此需要高质量的图像来验证信息的正确性。在本文中，提出并测试了一种基于多源深度学习的领域适应系统，以识别和验证产品包装通过食品生产线时作为验证过程一部分而拍摄的食品包装照片中使用日期信息的存在和易读性。这是通过合并新的损失函数并利用多源数据集来改善技术的通用性，以提取所有域的域不变表示并在一个公共特征空间中对齐源对和目标对的所有对分布来实现的，以及类的边界。所提出的系统在进行的实验中表现非常出色，用于自动化验证过程并减少可能会威胁公众健康并违反食品包装信息和准确性法律要求的标签错误。对我们的食品包装数据集进行的综合实验表明，所提出的多源深域适应方法可显着提高分类准确性，因此在食品制造控制系统中具有巨大的应用潜力和有益的影响。