What We Build
High-speed cameras mounted above your inspection table, capturing fabric at production speed. AI software trained on textile defects analyzes every frame. A monitoring dashboard shows defects in real-time with exact location marking.
The system detects holes, stains, weaving faults, shade variations, and 40+ other defect types. Each defect is logged with timestamp, position, and classification.
How It Works
Fabric passes under cameras at normal production speed. The AI processes images instantly and flags defects on the operator screen. Operators can stop the line or mark for later cutting. End of day, you get a defect report by roll, machine, and shift.
Who This Is For
Mills running 10+ looms with quality-sensitive buyers. If customer returns are cutting into margins, or if inspection is your bottleneck, this system pays for itself.
AI-Powered Fabric Defect Detection for Textile Mills
Boolean & Beyond deploys AI vision systems that inspect fabric at full loom speed, overcoming the 15–20% defect miss rate typical of manual inspection. Human inspectors struggle with fatigue, attention drift, and the high speed of modern looms, which leads to missed defects, rework, and buyer rejections—especially critical for export-focused mills in Coimbatore, Tirupur, Surat, and Bengaluru.
How the AI Inspection System Works
- High-resolution imaging: Line-scan cameras are mounted above your existing inspection frames, capturing every square centimetre of fabric in real time.
- Deep learning models: Models are trained on thousands of labelled images from your own production, covering defects such as:
- Holes
- Stains
- Broken picks
- Missing ends
- Shade variations
- Selvedge defects
- Pattern misalignments
- Real-time analysis: The AI processes each frame in milliseconds, detecting and classifying defects by type and severity.
- Roll mapping and reporting: Detected defects are mapped to their exact position on each roll, generating per-roll quality reports that support accurate grading and cutting decisions.
- Fabric-specific models: Separate models are trained and optimised for different fabric categories—cotton, polyester blends, denim, technical textiles—so each fabric’s unique defect patterns are handled precisely.
3-Phase Implementation Approach
Phase 1: Defect Library & Data Collection (Weeks 1–3)
- On-site study of your mill setup (fabric types, loom configurations, quality standards).
- Joint creation of a defect library aligned with your buyers’ requirements.
- Installation of high-resolution cameras on existing inspection frames.
- Collection of baseline image data across your production mix, including both good fabric and known defects.
Phase 2: AI Model Training & Validation (Weeks 4–6)
- Training of custom detection models using your captured fabric imagery.
- Validation against your grading criteria and buyer standards.
- Tuning of detection sensitivity to:
- Reliably catch critical defects.