Manufacturing · 2024
Manufacturing QC Visual Inspection
Automated visual inspection system using deep learning (YOLO + ResNet) to detect surface defects in real time — reducing false negatives by 94% and cutting QC labor costs by 60%.
Industrial Manufacturer
Manufacturing QC Visual Inspection
Client
Industrial Manufacturer
Industry
Manufacturing
Duration
8 months
Team
7
Results
94%
Defect Detection Accuracy
60%
QC Labor Cost Reduction
<12ms
Inference Per Frame
24/7
Automated Monitoring
Business Challenge
Manufacturing lines struggle with manual defect detection — slow, error-prone, and costly — while quality teams lack real-time visibility into production anomalies.
Our Solution
Automated visual inspection system using deep learning (YOLO + ResNet) to detect surface defects in real time, reducing false negatives by 94% and cutting QC labor costs by 60%.
CV Inspection Pipeline
- Image Capture — HD cameras @ 60fps
- Preprocessing — noise reduction, normalization
- Defect Detection — YOLOv8 inference <12ms/frame
- Result Output — dashboard alert + auto-reject
Tech Stack
- Python
- PyTorch
- YOLOv8
- OpenCV
- NVIDIA Triton Inference Server
- FastAPI
- Docker
- AWS S3
PythonPyTorchYOLOv8OpenCVNVIDIA TritonFastAPIDockerAWS S3
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