AI-Powered Quality Control for Textile Manufacturing
Computer vision system that detects fabric defects in real time, reducing quality escapes by 40% for a leading Surat textile manufacturer.
The Challenge
Shreeji Textiles was producing over 500,000 meters of fabric daily across multiple production lines. Their team of 40 manual inspectors could only sample-check approximately 15% of total output, meaning the majority of fabric shipped without individual inspection. Defect escape rates were averaging 3.2%, resulting in substantial customer complaints, returns, and rework costs exceeding INR 45 lakhs annually.
The company had previously evaluated off-the-shelf inspection systems from European vendors but found them prohibitively expensive and poorly adapted to the specific defect types common in synthetic fabric production. They needed a solution tailored to their fabric types, production speeds, and quality standards.
Additionally, any solution needed to integrate with their existing production management system and operate reliably in the factory environment with its temperature fluctuations, vibration, and dust.
Our Solution
Omeecron's team spent three weeks on the factory floor, documenting defect types, understanding production workflows, and collecting training data. We captured over 50,000 labeled images covering all major defect categories across different fabric types and colors.
We developed a custom deep learning model using a modified ResNet architecture optimized for high-speed inference on edge computing hardware. The system processes images at over 200 frames per second, keeping pace with the fastest production lines. Edge computing units installed at each inspection point ensure sub-100-millisecond detection latency, enabling real-time rejection of defective sections.
The integration layer connects the vision system to Shreeji's existing production management database, automatically tagging quality data to specific production batches, looms, and operators. A web-based dashboard built with React provides real-time monitoring accessible from any device on the factory network.
Project Overview
Shreeji Textiles, one of Surat's largest synthetic fabric manufacturers, needed a modern quality control solution to keep pace with their expanding production capacity. Their manual inspection process could not scale with growing output, and inconsistent defect detection was leading to costly customer returns and damaged brand reputation. Omeecron designed and deployed an AI-powered computer vision system that inspects every meter of fabric in real time, detecting defects with over 96% accuracy.
Technical Approach
The solution uses high-resolution line-scan cameras positioned at key inspection points along the weaving and finishing lines. Images are processed by a custom-trained convolutional neural network that identifies over 25 defect types including broken threads, weave pattern errors, stains, holes, and color inconsistencies. The system integrates directly with the factory's production management software, logging defect data against specific looms, beams, and production batches for comprehensive traceability.
A real-time dashboard provides production managers with live quality metrics, defect trend analysis, and alerts when defect rates exceed thresholds. The system also generates shift-wise and machine-wise quality reports that feed into the company's continuous improvement program.
System Preview
Explore the key screens and dashboards we designed and developed.
AI Vision Feed — Analyzing fabric in real time
Defect Distribution
Total: 23 defects in 12,847 metersLoom L-01
Loom L-02
Loom L-03
Loom L-04
Shift B Report
8:00 AM - 4:00 PM | Operator: R. Kumar
Defects by Hour
High defect rate detected on Loom L-03 — 5 defects in 10 minutes. Machine paused for inspection.
Thread tension variance on L-02 exceeding threshold. Calibration recommended.
Shift B started. All 4 looms operational. AI monitoring active.
Color consistency deviation on L-01 — dye batch #DB-442 flagged for review.
Daily maintenance check completed. All looms passed. No issues found.
Measurable Outcomes
Defect escape rate reduced from 3.2% to 1.1% within six months
fabric now inspected compared to previous 15% sample rate
defect detection accuracy across all defect categories
Customer complaint rate decreased by 58%
Annual savings of INR 32 lakhs from reduced returns and rework
Quality data analytics enabled root cause identification for top defect types, l...
System processes 500,000+ meters of fabric daily without human intervention
Have a Similar Project in Mind?
Let us discuss how we can build a solution tailored to your specific needs. Free consultation included.
Get Free Consultation