A non-destructive, autoencoder-based approach to detecting defects and contamination in reusable food packaging.

Today, environmental sustainability is one of the most critical issue. Hence, the food service industry is actively seeking ways to minimize its ecological footprint. One solution to address this issue is the adoption of reusable foodware in the food service industry. This approach requires a careful process for the collection and thorough cleaning of the foodware, ensuring it can be safely reused. However, reusable foodware might be damaged during the collection process, which can pose food safety hazards for customers. Additionally, there are cases where the cleaning process might not effectively remove all contaminants and therefore cannot be reused after the washing process. To ensure consumer safety, a manual inspection is typically conducted after the cleaning process. However, this step is labor-intensive and prone to human error, particularly as workers' attention may decrease over extended periods. Consequently, the adoption of precise and automated methods for detecting defects and contaminants is becoming crucial, not only to ensure safety but also to achieve scalability and enhance cost-efficiency in the pursuit of environmental sustainability. In our research, we explore various data augmentation strategies and the application of knowledge transfer from various samples of reusable food containers. This method only requires few images from a clean sample to teach the network about normal patterns, and to detect defects by identifying irregular details that do not exist in normal samples. This allows us to rapidly deploy the detection system even with a limited number of collected samples. Experimental results demonstrate the effectiveness of our approach in detecting both contamination and cracks on food containers.