Opsio Inspection System

Supreme Technologies > Opsio Inspection System
About Case Study

What is the Opsio Inspection System?

The Opsio Inspection System is an advanced, AI-driven platform built to automate end-to-end PCB (Printed Circuit Board) quality verification. It streamlines the entire inspection workflow by capturing high-resolution images, analyzing components in real time, and ensuring every board meets strict engineering standards. Acting like an intelligent assistant, it helps manufacturers detect defects faster, reduce human error, and maintain consistent production quality. One of its key strengths is the integrated AI training and annotation pipeline. With the help of Supreme Technologies, Opsio uses Label Studio for precise labeling and a custom YOLO-based training engine, enabling teams to build and upgrade accurate defect-detection models without complex engineering efforts. The system tracks missing components, alignment issues, and other visual defects, automating decisions that would otherwise require manual review.

This unified approach allows manufacturers to shorten inspection time, maintain full traceability, and standardize quality checks. With real-time insights and automated verification, Opsio supports scalable production while ensuring every PCB meets required quality expectations.

Developers Involved

2 Specialists

Business Type

Development

Time Spent

Approx. 8 weeks

Completed Date

20-12-2024

Following

Technologies Used

Frontend Interface

Vue.js, TailwindCSS

Backend & AI Orchestration

Django, Python, YOLO Framework

Annotation & Model Training

Label Studio, YOLOv8 Training Engine

Hardware & Deployment

Industrial Camera, Fanless PC, Touchscreen Display
Our Approach

Solution We Offered

Admin Model Training Workflow

Admins can quickly update AI models through a simple Training Dashboard. They upload YOLO annotations from Label Studio, start automated training, review the generated .pt model, and deploy it directly into the Opsio Inspection System. This keeps the AI accurate and up to date, without needing engineering support.

Real-Time Processing Performance

Optimized high-resolution image processing across multiple stations to maintain sub-2-second speeds, implementing efficient AI pipeline architecture and resource allocation to eliminate bottlenecks and ensure accurate defect detection without disrupting production workflows.

Annotation Quality and Consistency

Created parameterized Selenium scripts to test across multiple browsers and screen resolutions, ensuring responsive design and seamless user experience across all devices and platforms.

System Integration Complexity

Integrated Label Studio, training pipeline, and live inspection system with seamless data flow. Created robust APIs ensuring version control and backward compatibility across all deployment environments and platforms.

Project Goals

01. End-to-End PCB Inspection Automation
Designed and developed an intelligent inspection pipeline capable of capturing high-resolution PCB images, detecting components, identifying defects, and generating instant pass/fail results without human interpretation.
Built a structured training lifecycle using labeled datasets and YOLO-powered learning models, ensuring precise defect detection even for fine-pitch and complex PCB layouts.
Configured industrial-grade imaging hardware and on-device inference to ensure stable, low-latency performance suitable for production lines, labs, and field environments.
Implemented automatic logging for each inspected board, including timestamps, defect types, component counts, and workflow outcomes, enabling analytics, compliance reporting, and progressive quality improvement.
  • End-to-End PCB Inspection Automation

Challenges We Faced

Inconsistent Image Quality

Different lighting, angles, and surfaces across production lines caused uneven illumination and micro-shadows, reducing detection consistency.

Early YOLO Model Instability

Initial datasets had gaps and imbalances, leading to missed micro-components, false positives, and unstable accuracy.

Hardware-Based Latency Issues

Fanless industrial PCs struggled with real-time inference due to limited GPU power, thermal slowdowns, and frame delays.

Hardware-Based Latency Issues

Fanless industrial PCs struggled with real-time inference due to limited GPU power, thermal slowdowns, and frame delays.

Multiple PCB Variants

Different PCB types with varying densities made it challenging to maintain uniform accuracy without retraining for every board.

Multiple PCB Variants

Different PCB types with varying densities made it challenging to maintain uniform accuracy without retraining for every board.

Need for Large, High-Quality Dataset

Achieving stable, generalizable detection required significant dataset expansion, better annotations, and continuous refinement.

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Our Impact

The Result
Outstanding Digital Transformation

The Opsio Inspection System delivered faster, more consistent, and highly accurate PCB inspections by replacing manual verification with real-time AI detection. Inspection time was reduced significantly, defect-identification accuracy improved, and operator-dependent variation was eliminated. Automated logging and centralized reporting enabled better tracking, analysis, and continuous process improvement, while stable hardware-integrated inference ensured reliable performance across production environments. Overall, the solution proved scalable, efficient, and dependable for modern PCB quality control.

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