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Letting Data Speak, AI Act!
Case Study
Automated PCB Defect Detection Using Advanced Computer Vision
About the Client
A leading electronics manufacturing company specializing in high-volume printed circuit board production for industrial automation equipment. The client operated multiple production facilities with stringent quality requirements and faced increasing pressure to maintain zero-defect manufacturing standards while scaling production capacity to meet growing market demand.
Challenge
The client's traditional manual quality inspection process created significant operational bottlenecks and quality control issues that threatened their competitive position in the electronics manufacturing sector.
ā¢ Inconsistent Detection Performance: Human inspectors struggled to consistently identify defects across complex PCB layouts, particularly missing components, soldering faults, and placement errors that could lead to device failures in the field.
ā¢ Manual Process Limitations: Inspector fatigue led to inconsistent detection rates, while the complexity of modern PCB designs with dense component arrangements made visual inspection increasingly unreliable.
ā¢ Production Impact: The labor-intensive process required specialized training and created production delays that impacted delivery schedules.
ā¢ Business Risk: Continued reliance on manual inspection threatened both profitability and customer satisfaction through mounting pressure to improve quality control while reducing inspection costs and cycle times.
Key Results
Reduced inspection time per PCB by over 80%, enabling faster production throughput and shorter delivery cycles
Established robust detection capability across diverse lighting conditions and complex PCB layouts, eliminating previous system limitations
Achieved 74% precision and 72% recall in automated defect classification, significantly improving detection consistency compared to manual inspection
Solution
JashDS implemented a sophisticated automated visual inspection system leveraging advanced computer vision technology to transform the client's quality control process.
ā¢ YOLO-V8-Large Architecture: The solution employed the YOLO-V8-Large deep learning architecture, specifically fine-tuned for PCB defect detection, utilizing a comprehensive patch-based comparison methodology that analyzed test PCBs against master reference images.
ā¢ Training and Dataset Development: The technical implementation centered on training the model using a carefully curated dataset of high-definition PCB images standardized, enabling precise component-level analysis across entire board surfaces. 5008 training images andĀ 1252 validation images were used.
ā¢ Algorithmic Approach: The core algorithm performed component detection on both master and test image patches using the trained YOLO model, then executed location-based matching to identify discrepancies and automatically flag missing components with precise bounding box annotations.
ā¢ Multi-Scenario Handling: The deployment architecture addressed multiple challenging inspection scenarios, including edge defects, lighting variations, small component detection, and multi-angle inspection requirements while maintaining consistent accuracy under varying production conditions.
Test Results:
Defect Detection Example
Defect Localization
Accurate defect pinpointing The model identifies and marks defects precisely. | High confidence scores Reliable predictions for PCB defects | Clear visualization Bounding boxes assist inspection understanding. |
Complex PCB Analysis
Multiple defect types Detects soldering and component faults | Handling dense board Maintains accuracy on cluttered PCB designs |
Lighting Variation Handling
Unaffected by lighting variations | Handles reflective surfaces | Fast inference times |
Small Defect Detection
The model was able to detect the missing LED based on the master image.
Multi-Angle Inspection
Final Validation
Summary of Model Performance High precision and recall in real-world testing | Robustness Effective on diverse PCB defects and conditions |
Technologies Used
YOLO-V8-large (YOLOv8l) object detection framework
Python computer vision libraries (OpenCV, PIL)
PyTorch deep learning framework
CUDA GPU acceleration for real-time inference
Docker containerization for production deployment
REST API integration for manufacturing execution systems
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