Real-Time Inventory Analysis Using Jetson Nano with Object Detection and Analysis
Retail product recognition on grocery shelf photographs is a challenging task due to the variety of products and lighting conditions. This research proposes a novel method for completing this task efficiently and in a short amount of time. The object detection and recognition module is deployed on a...
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| Main Authors: | , , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2023
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/87646 |
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| Summary: | Retail product recognition on grocery shelf photographs is a challenging task due to the variety of products and lighting conditions. This research proposes a novel method for completing this task efficiently and in a short amount of time. The object detection and recognition module is deployed on a jetson Nano board, which is trained to trace a path for real time object image capture. The proposed method consists of two stages: detection and recognition. The detection stage is performed by a generic product detection module that has been trained on a particular class of products (e.g., tobacco packages). This is accomplished using the Viola and Jones Cascade Object Detection Framework. The recognition stage is performed using two FCN designs: U-Net and Fully Convolutional Regression Network (FCRN). We extract both shape and color information by applying feature-level fusion to two distinct descriptors computed using the bag of words method. We evaluated our method on a dataset of grocery shelf photographs. We achieved a detection accuracy of 95% and a recognition accuracy of 90%. The proposed method is efficient and accurate, and it can be used for a variety of applications, such as inventory management, price tracking, and fraud detection. |
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| ISBN: | 978-1-83768-322-2 |
| DOI: | 10.5772/intechopen.112269 |