Deep Learning for Efficient Retail Shelf Stock Monitoring and Analysis

dc.contributorAalto Universityen
dc.contributor.advisorDuda, Andrzej
dc.contributor.advisorDecor, Guillaume
dc.contributor.authorLachhab, Walid
dc.contributor.schoolSähkötekniikan korkeakoulufi
dc.contributor.supervisorPajarinen, Joni
dc.description.abstractThis thesis explores the automation of stock management in retail stores, with a specific focus on stores specializing in the sale of fruits and vegetables. Traditionally, these stores have relied on manual stock management methods, involving periodic inspections to maintain product availability. In response, this study proposes the application of Deep Learning techniques, particularly object counting models, to automate stock management. The automation process comprises two key steps. Initially, a camera positioned above a shelf of fruits and vegetables captures an image, which is processed to identify boxes containing fruits and vegetables, along with their respective categories. Afterward, a Deep Learning counting model is employed to provide an estimation of the number of objects present within each box. These estimations can then be continuously monitored or subjected to analysis to optimize store operations. The research encompasses four distinct data scenarios: supervised learning, semi-supervised learning, few-shot learning, and zero-shot learning. Within each scenario, existing object counting methods are evaluated using object detection and density estimation methodologies. The primary goals of this research are to establish an experimental setup for assessing object counting models across different learning frameworks, evaluate their performance in various scenarios, and analyze the practical strengths and limitations of these techniques in retail store environments. Key findings from the study highlight the superior performance of YOLO models, especially YOLOv5, in supervised learning scenarios, striking a balance between speed and model size. In semi-supervised learning, the application of the Efficient-Teacher approach to YOLO models enhances performance with limited labeled data. Zero-shot learning, specifically the CLIP-Count method offering a balance between speed and acceptable error rates, is recommended for data-scarce environments with sufficient computational resources. While few-shot learning, represented by the SAFECount approach, remains as the last option due to its relatively higher error, and it is suggested for situations with limited data and computational resources. Furthermore, our study reveals that improving the counting model's performance can be achieved through the removal of certain complex-shaped categories that present counting difficulties, such as grapes and hot peppers. Additionally, merging categories of fruits and vegetables with similar appearances emerges as a viable strategy for optimization. Overall, this thesis offers practical insights into automating stock tracking in retail stores. It emphasizes the importance of selecting the right learning framework and model based on specific operational needs and constraints such as data availability, providing valuable guidance to improve stock management efficiency in diverse data scenarios.en
dc.programmeMaster’s Programme in Communications and Data Sciencefi
dc.programme.majorCommunications Engineering and Data Sciencefi
dc.subject.keyworddeep learningen
dc.subject.keywordcomputer visionen
dc.subject.keywordobject countingen
dc.subject.keywordretail automationen
dc.subject.keywordstock trackingen
dc.subject.keywordbenchmarking studyen
dc.titleDeep Learning for Efficient Retail Shelf Stock Monitoring and Analysisen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
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