Deep learning-based item classification for retail automation

Ling, Ji Xiang (2025) Deep learning-based item classification for retail automation. Final Year Project, UTAR.

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Abstract

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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