Automated Quality Control via Deep Learning
Deployed a convolutional neural network pipeline for real-time product defect detection on a manufacturing production line.
CHALLENGE
Manual quality inspection at production speed caused missed defects and line slowdowns, resulting in costly downstream failures.
APPROACH
Trained a custom CNN architecture on annotated defect imagery, deployed on edge hardware with GPU acceleration for sub-frame inference.
OUTCOME
Achieved 99.7% defect detection accuracy at full production line speed, eliminating the need for manual inspection and reducing downstream defect costs.