Deep learning-based item classification for retail automation
This project focuses on developing a deep learning-based system for retail item classification. The project aims to improve the efficiency and recognition accuracy in retail operations. By leveraging the Convolutional Neural Networks (CNNs) and computer vision techniques such as object detection...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7132/1/fyp_CS_2025_LJX.pdf http://eprints.utar.edu.my/7132/ |
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| Summary: | This project focuses on developing a deep learning-based system for retail item classification.
The project aims to improve the efficiency and recognition accuracy in retail operations. By
leveraging the Convolutional Neural Networks (CNNs) and computer vision techniques such
as object detection and image segmentation. The project involved creating a comprehensive
dataset of retail items, which was preprocessed using data augmentation techniques to enhance
model generalization. The CNN model was optimized for both accuracy and speed,
incorporating regularization techniques such as dropout and batch normalization. Real-time
processing was achieved through the integration of object detection algorithms like YOLO and
image segmentation techniques. The final system was deployed and tested in a simulated retail
environment, where its performance was evaluated using metrics such as accuracy, precision,
recall and F1-score. This project also addresses the limitations of current methods and provides
a scalable solution for modern retail automation. Furthermore, this project contributes to the
field of retail automation by offering a scalable and adaptable solution for real-time item
classification. Additionally, it provides a practical framework for deploying deep learningbased classification systems in real-world retail settings, enhancing operational efficiency and
setting new standards for accuracy. |
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