Defect Detection with Deep Learning for India

GitHub is full of impressive deep learning defect detection notebooks — YOLOv8 for object detection, ResNet for classification, U-Net for segmentation — but the gap between a notebook demo and a reliable production system is enormous. Indian manufacturers who try to deploy these models in-house often find themselves stuck with models that work in the lab but fail on the production line due to lighting variations, camera angles, class imbalance, and inference speed limitations. Opsio bridges this gap with production engineering expertise. We evaluate open-source architectures, select the best starting point for your specific defect type and production environment, then customise the model with your data, optimise for edge inference, build MLOps pipelines for continuous improvement, and integrate with your PLC/SCADA and MES systems. The result is a system that runs reliably 24/7 without data scientist intervention.

Our team has productionised defect detection systems across Indian manufacturing verticals — taking models from initial proof-of-concept to full-scale deployment in as little as 6 weeks. We handle everything from camera selection and lighting design to model training, edge deployment, and ongoing monitoring.