Automated Ampule Inspection System with Deep Learning
Detection & segmentation · Quality assurance · Production deployment
Overview
Deep learning-based ampule inspection system for defect detection and quality control. Automated pipeline ensures pharmaceutical-grade quality standards with high reliability.
Problem
- Pharmaceutical industry requires stringent quality standards.
- Various defect types: cracks, particles, contamination, fill level.
- Transparent glass ampules create challenging inspection conditions.
- Need for reliable automation to replace manual inspection.
Solution
- Deep learning pipeline for ampule defect detection.
- Combined detection and segmentation for comprehensive QA.
- Data augmentation for robustness under varying conditions.
- Production-validated performance metrics.
My Role
- Designed inspection pipeline for ampule defects.
- Validated model performance under production constraints.
- Improved reliability via data augmentation and tuning.
Implementation
Language: Python
Libraries:
PyTorch
OpenCV
Results Gallery
End Result
- Automated, production-ready ampule inspection pipeline.
- High reliability meeting pharmaceutical quality standards.
- Reduced manual inspection costs and improved consistency.