Computer Vision Defect Detection For an Electronics Manufacturer
Challenge
Our client is a Saudi-based electronics manufacturer producing laptops for the MEA region in partnership with one of the global tech leaders. Years earlier, we had created digital twins of their factories, so when new challenges surfaced, the client turned to us again.
The company had long moved from fully manual defect detection on the production line to an automated, non-contact product quality inspection. However, their automated optical inspection (AOI) in the conveyor video surveillance system was too demanding, rigid, and costly, posing numerous limitations.
Our client is a Saudi-based electronics manufacturer producing laptops for the MEA region in partnership with one of the global tech leaders. Years earlier, we had created digital twins of their factories, so when new challenges surfaced, the client turned to us again.
The company had long moved from fully manual defect detection on the production line to an automated, non-contact product quality inspection. However, their automated optical inspection (AOI) in the conveyor video surveillance system was too demanding, rigid, and costly, posing numerous limitations.
Solution
The team designed and deployed an AI-based computer vision defect detection system using a YOLOv8 object detection model optimized for real-time performance:
Improved and augmented dataset for better defect coverage
Transfer learning on labeled data
Calibration of detection thresholds and advanced model enhancements (BiFPN, DySample etc.)
Seamless integration with the manufacturing execution system (MES)
Continuous improvement through cloud connectivity.
The team designed and deployed an AI-based computer vision defect detection system using a YOLOv8 object detection model optimized for real-time performance:
Improved and augmented dataset for better defect coverage
Transfer learning on labeled data
Calibration of detection thresholds and advanced model enhancements (BiFPN, DySample etc.)
Seamless integration with the manufacturing execution system (MES)
Continuous improvement through cloud connectivity.
Results
Operational impact:
+ 27,4% accuracy of production line inspections
+ 24% in production throughput due to immediate defect detection
< 2% false positive rate
< 0,5% discarded items
– 67% defect-related warranty claims
Financial impact:
– $1.2 million in waste-related costs
+ $2.4 million annually due to the drop in warranty claims
– 26% in quality control labor costs
Operational impact:
+ 27,4% accuracy of production line inspections
+ 24% in production throughput due to immediate defect detection
< 2% false positive rate
< 0,5% discarded items
– 67% defect-related warranty claims
Financial impact:
– $1.2 million in waste-related costs
+ $2.4 million annually due to the drop in warranty claims
– 26% in quality control labor costs