Fabric Defect Detection with Deep Learning in India

India is the world's second-largest textile producer, with the industry contributing 2.3% to GDP and employing over 45 million people. Yet fabric quality inspection in most Indian mills remains manual — trained inspectors visually scanning fabric on inspection frames at limited speeds, catching only 60–70% of defects. Missed defects result in rejected shipments, price penalties from export buyers, and damaged reputation in global markets where quality expectations are unforgiving. Deep learning fabric inspection changes this equation. Opsio deploys computer vision systems with line-scan cameras and custom-trained models that inspect every square centimetre of fabric at full loom or inspection machine speed. Our models detect weaving defects (broken threads, missing picks, float, holes), knitting defects (dropped stitches, needle lines, barre), dyeing defects (shade variation, spots, streaks), and printing defects (misregistration, colour bleeding, pattern distortion) with 95%+ accuracy.

We have deployed fabric inspection systems in Indian cotton mills, synthetic textile plants, denim manufacturers, and technical textile producers. Each system is custom-trained on the specific fabric types and defect categories relevant to the mill, with automatic grading that classifies fabric into quality grades based on defect density and severity — enabling data-driven pricing and buyer allocation decisions.