Abstract:

This project focuses on developing an automated system for cloth quality grading using advanced deep learning techniques. The system employs deep learning models, particularly convolutional neural networks (CNNs), to detect fabric defects and classify fabric types, such as cotton, silk, wool, and other textile materials, by analysing high-resolution images. It assigns a quality grade based on detected features, enhancing the accuracy, consistency, and efficiency of fabric quality assessment while reducing reliance on manual inspection. Additionally, the website facilitates direct interaction between buyers and sellers, promoting seamless communication for inquiries regarding products, fostering trust and transparency. This deep learning-based solution provides an efficient, scalable, and automated method for fabric quality grading and type classification, transforming quality control in textile manufacturing.

Introduction:

The textile industry is one of the largest global industries, with quality control playing a critical role in ensuring customer satisfaction and minimizing manufacturing defects. Traditional methods of fabric quality inspection are labor-intensive, time-consuming, and prone to human error. Advances in deep learning and image processing offer transformative opportunities to automate and optimize this process. By leveraging convolutional neural networks (CNNs), this project aims to detect fabric defects and classify textiles efficiently, enhancing operational accuracy and scalability.

Problem Statement:

Traditional fabric quality inspection methods rely heavily on manual processes, leading to inconsistencies, inefficiencies, and high labor costs. This results in delayed quality checks, increased chances of defective products reaching the market, and reduced trust among buyers. Furthermore, a lack of direct communication between textile manufacturers and buyers complicates market integration, impacting transparency and sales efficiency.

Existing System and Disadvantages:

The existing systems for fabric quality inspection predominantly rely on manual labor or semi-automated methods. While some industries have adopted machine vision systems, these solutions often lack advanced defect detection capabilities, especially for subtle or complex defects.

• Disadvantages:
• Inconsistent and error-prone manual inspection.
• High operational costs due to dependency on skilled labor.
• Limited scalability and speed.
• Lack of real-time feedback and classification accuracy.
• Poor integration between manufacturers and buyers for seamless product inquiries.

Proposed System and Advantages:

The proposed system leverages deep learning, particularly CNNs, for automated fabric defect detection and classification. It also integrates a user-friendly website to connect buyers and sellers, facilitating transparent communication and seamless transactions.

Advantages:

• Enhanced accuracy and consistency in defect detection.
• Automated, real-time inspection reducing labour costs.
• Scalable solution suitable for large-scale manufacturing.
• Improved transparency and trust through buyer-seller communication.
• Comprehensive classification of fabric types and quality grading.

Modules:

• Image Acquisition: High-resolution images and videos of fabric samples are captured for analysis.
• Pre-processing: Images and videos are pre-processed to enhance defect visibility and standardize input formats.
• Defect Detection: CNN-based models identify and detect the defects in fabrics.
• Fabric detection: detection of fabric types such as cotton, silk, wool, etc., using deep learning models.
• Quality Grading: Assignment of quality grades based on defect analysis and classification results.
• Market Integration: Development of a website to connect buyers and sellers for seamless communication and transactions.

1. Admin Module

• Login & Dashboard: Admins can securely log in and view an overview of platform activities.
• User Management: Manage and verify registration of sellers and buyers.

2. Seller Module

• Registration & Profile Setup: Sellers register and set up their fabric store profile.
• Upload Products (Image/Video): Sellers can upload cloth images or videos for defect detection.
• Automatic Defect Detection: The system analyzes uploads using the trained CNN model and classifies fabric as “Defective” or “Non-defective.”
• Marketplace Listing: Non-defective products are automatically listed on the marketplace. Defective ones can optionally be listed at discounted prices or rejected.

3. Buyer Module

• Registration & Profile: Buyers create accounts and manage their profiles.
• Search & Filter Products: Browse cloth listings based on categories, price, and defect status (e.g., defect-free or discounted defective).
• View Product Quality in Detection Images or Videos: Buyers can see AI-generated quality tags such as ‘Defective’ or ‘Non-defective’ on product listings.

Algorithms:

• Convolutional Neural Networks (CNNs): Used for defect detection and fabric classification.
• Image Processing Techniques: Applied for image enhancement and preprocessing.

Software and Hardware Requirements:

• Software:
• Python (TensorFlow, Keras, OpenCV)
• Web development tools (HTML, CSS, JavaScript, Flask)
• Database (MySQL)
• Hardware:
• High-resolution cameras for image acquisition.
• GPUs for deep learning model training and inference.
• Servers for hosting the website and managing data.

Conclusion and Future Enhancement:

The proposed system offers a robust, automated solution for fabric defect detection and quality grading, enhancing efficiency and scalability in the textile industry. The integration of a buyer-seller communication platform further promotes trust and transparency, revolutionizing the market dynamics. Future enhancements include:

• Expanding the system to detect a broader range of textile materials and defect types.
• Incorporating advanced AI techniques such as reinforcement learning for improved defect detection.
• Developing mobile applications to enable on-the-go fabric quality checks.
• Implementing blockchain technology for traceability and transparency in the textile supply chain.