Straive Computer Vision AI PoC for Defect Detection

Straive's AI-powered solution, Defect detection, takes your manufacturing processes to the next level. It is designed to boost efficiency and accuracy with its multiple applications, including inventory management, defect detection, shop floor hygiene and safety, automated inspection, and adherence to SOPs. Say goodbye to manual inspection methods and hello to significant advantages.

This solution is applicable across industries including Pharma, Automotive, Food and Beverage, Semiconductor and Electronics, Printing and Packaging.

Straive's Defect Detection solution delivers the following benefits::

• Manufacturers benefit from reduced labor and other operational costs
• Production volume increases without compromising quality
• Preventing defective parts by delaying assembly lines through early error detection
• Improved manufacturing efficiency and reduced turnaround times
• Delivering better accuracy rates than the human eye
• Identifying patterns in historical data to predict and improve future production processes

Straive's AI powered Defect Detection solution offer includes End-to-End implementation services to leverage the full power of vision hardware and Azure AI and Data Services :

Discovery & Requirements • Map defect types, acceptance criteria, and integration points within the production line • Define SLAs for detection accuracy, latency, and throughput

Data Ingestion Pipeline • Deploy cameras with IoT Edge modules to capture images on-premise • Stream images via Azure IoT Hub into Blob Storage for raw data archival

Data Labeling & Annotation • Leverage Straive’s annotation platform to tag defect regions at scale • Validate labels against QA benchmarks to ensure model quality

Data Processing & Augmentation • Use Azure Databricks to normalize image size, balance classes, and apply synthetic augmentations (rotation, lighting variations) • Persist processed datasets in Data Lake for reproducibility

Model Training & Validation • Train object detection models in Azure Custom Vision using labeled datasets • Export best-performing models into Azure ML for further hyperparameter tuning and cross-validation

Deployment & Inference • Package models as Docker containers and push to Azure Container Registry • Deploy to Azure Kubernetes Service for cloud-scale inference or to IoT Edge for on-device real-time detection

MLOps & Continuous Learning • Implement Azure ML pipelines to automate periodic retraining with new images • Monitor model drift and trigger alerts via Azure Monitor when accuracy degrades

Integration & Visualization • Connect inference outputs to MES using Azure Functions or Logic Apps • Build Power BI dashboards to track defect trends, root causes, and KPI impacts

Ongoing Optimization • Analyze false positives/negatives and refine annotation guidelines • Tune edge-device compute allocations and model compression for latency targets

Price published in the offer is Estimated.