Click and Cart Fashion Classification and Object Detection

In the rapidly evolving e-commerce industry, fashion classification and object detection play pivotal roles in enhancing user experience and improving operational efficiency. "Click & Cart" is an advanced system designed to address challenges in fashion retail by combining state-of-the-art computer vision techniques for object detection with machine learning algorithms for fashion classification. The system leverages deep learning models, particularly Convolutional Neural Networks (CNNs), to classify and detect fashion items such as clothing, accessories, and footwear from product images. Additionally, it uses object detection algorithms like YOLO (You Only Look Once) and Faster R-CNN to pinpoint key components within images, enabling the extraction of detailed information such as color, fabric, and style. By integrating both classification and detection, Click & Cart offers personalized recommendations, assists in virtual try-ons, and streamlines inventory management. This approach is designed to improve customer satisfaction through more accurate searches, real-time suggestions, and a more intuitive shopping experience. Furthermore, Click & Cart presents a scalable solution that can be applied across various platforms, ensuring consistency in both online and mobile shopping environments

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-04-15
DOI
https://doi.org/10.22214/ijraset.2025.68667
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Click and Cart Fashion Classification and Object Detection

Nandini Khandade
International Journal for Research in Applied Science and Engineering Technology
Industrial Vision Systems and Defect Detection
article

Click and Cart Fashion Classification and Object Detection

Nandini Khandade
article en

Abstract

In the rapidly evolving e-commerce industry, fashion classification and object detection play pivotal roles in enhancing user experience and improving operational efficiency. "Click & Cart" is an advanced system designed to address challenges in fashion retail by combining state-of-the-art computer vision techniques for object detection with machine learning algorithms for fashion classification. The system leverages deep learning models, particularly Convolutional Neural Networks (CNNs), to classify and detect fashion items such as clothing, accessories, and footwear from product images. Additionally, it uses object detection algorithms like YOLO (You Only Look Once) and Faster R-CNN to pinpoint key components within images, enabling the extraction of detailed information such as color, fabric, and style. By integrating both classification and detection, Click & Cart offers personalized recommendations, assists in virtual try-ons, and streamlines inventory management. This approach is designed to improve customer satisfaction through more accurate searches, real-time suggestions, and a more intuitive shopping experience. Furthermore, Click & Cart presents a scalable solution that can be applied across various platforms, ensuring consistency in both online and mobile shopping environments

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(4)
Openalex Percentile: Top 90%
Industrial Vision Systems and Defect Detection
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