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

IF 6.2 2区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY
Anh Minh Truong, Hiep Quang Luong
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引用次数: 0

Abstract

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.

Abstract Image

基于自动编码器的非破坏性方法检测可重复使用食品包装中的缺陷和污染
如今,环境的可持续发展是最关键的问题之一。因此,餐饮服务业正在积极寻找各种方法,以最大限度地减少其生态足迹。解决这一问题的办法之一就是在餐饮业中采用可重复使用的餐具。这种方法要求对餐具进行仔细的收集和彻底的清洁,以确保其可以安全地重复使用。然而,可重复使用的食品容器可能会在收集过程中损坏,从而对顾客的食品安全造成危害。此外,在某些情况下,清洗过程可能无法有效去除所有污染物,因此在清洗后无法再次使用。为确保消费者安全,通常会在清洗过程后进行人工检查。然而,这一步骤是劳动密集型的,而且容易出现人为错误,特别是工人的注意力可能会随着时间的延长而下降。因此,采用精确的自动化方法检测缺陷和污染物变得至关重要,这不仅是为了确保安全,也是为了实现可扩展性和提高成本效益,以追求环境的可持续发展。在我们的研究中,我们探索了各种数据增强策略以及从各种可重复使用食品容器样本中进行知识转移的应用。这种方法只需要从干净的样本中获取少量图像,就能让网络了解正常模式,并通过识别正常样本中不存在的不规则细节来检测缺陷。这样,即使收集的样本数量有限,我们也能快速部署检测系统。实验结果表明,我们的方法在检测食品容器上的污染和裂纹方面非常有效。
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来源期刊
Current Research in Food Science
Current Research in Food Science Agricultural and Biological Sciences-Food Science
CiteScore
7.40
自引率
3.20%
发文量
232
审稿时长
84 days
期刊介绍: Current Research in Food Science is an international peer-reviewed journal dedicated to advancing the breadth of knowledge in the field of food science. It serves as a platform for publishing original research articles and short communications that encompass a wide array of topics, including food chemistry, physics, microbiology, nutrition, nutraceuticals, process and package engineering, materials science, food sustainability, and food security. By covering these diverse areas, the journal aims to provide a comprehensive source of the latest scientific findings and technological advancements that are shaping the future of the food industry. The journal's scope is designed to address the multidisciplinary nature of food science, reflecting its commitment to promoting innovation and ensuring the safety and quality of the food supply.
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