Textile Materials Quality Control System
Tech Stack
Project Description
An AI-driven quality control system using computer vision and deep learning models for fabric manufacturing, achieving 95% defect detection accuracy.
This innovative quality control system for textile manufacturing leverages computer vision and deep learning to detect and classify fabric defects with unprecedented accuracy.
The system achieved 95% defect detection accuracy, significantly improving production efficiency by 35%. By integrating high-resolution cameras with edge computing capabilities, the solution provides real-time defect classification, reducing detection time by 50%.
The architecture includes a distributed network of cameras that feed into a central processing system, where custom-trained neural networks analyze the fabric in real-time. An intuitive user interface allows operators to monitor production quality, review detected defects, and generate comprehensive reports.
This solution has transformed quality control processes in textile manufacturing, reducing waste, improving product quality, and enhancing operational transparency.
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