Robot Vision System for Retail Shelf Monitoring

2026
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Authors: Abhishek V. Latha; Mohammad Rahimipour; Adel Merabet
Published in: Robotics, Computer Vision and Intelligent Systems
Publisher: Springer Nature Switzerland

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Abstract

This chapter explores the implementation of a robot vision system for automated shelf monitoring in retail stores. The system utilizes deep learning tools, specifically YOLOv5 for object detection and MiDaS for depth estimation, to ensure on-shelf availability and tag accuracy. The robot vision system includes a mobile platform for navigation and a vision tower with multiple cameras for capturing images of products on shelves. The system processes these images to detect, classify, and verify tags, as well as to assess on-shelf availability. Experimental results demonstrate the system's accuracy in a laboratory setting, with future work aimed at scaling the solution for real retail environments. The chapter provides a comprehensive overview of the system's components, methodologies, and experimental outcomes, offering valuable insights for professionals in the field.

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Metadata
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Title: Robot Vision System for Retail Shelf Monitoring
Authors: Abhishek V. Latha
Mohammad Rahimipour
Adel Merabet
Publisher: Springer Nature Switzerland
Book: Robotics, Computer Vision and Intelligent Systems
Print ISBN: 978-3-032-00985-2

Electronic ISBN: 978-3-032-00986-9

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-032-00986-9_7

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