Case Study
Computer Vision for Defect Detection
Overview
CLIENT NDA INDUSTRY Manufacturing SERVICE Edge Computing / Machine Learning
Our Client
A regional PVC pipe manufacturer supplying construction materials to hardware stores and large-scale infrastructure projects. The company produces a wide range of pipe sizes used for drainage, water supply, and conduit installations.
Challenge
The client faced quality control challenges that resulted in increased product waste and rejected shipments. Surface cracks, often caused during extrusion cooling, were difficult to detect manually. Uneven cuts led to inconsistent lengths and jagged edges, contributing to product rejection. Additionally, diameter variations caused fitting issues for clients in construction projects, further impacting overall product reliability. These issues caused:
Product Returns and Rework Costs: Increasing customer dissatisfaction and financial losses.
Manual Inspection Bottlenecks: Quality control staff couldn’t keep up with the production speed.
Inefficiencies in Machine Calibration: Variations were often noticed after bulk production runs, leading to significant waste.
Solution: Real-Time Defect Detection
To address these challenges, we developed and implemented an affordable, edge-based Computer Vision system for real-time defect detection and quality control on the production line. It works by following workflow:
Two industrial cameras was mounted along the production line, positioned for both:
LED diffused lighting was added to eliminate glare and enhance defect visibility on the reflective PVC surface.
An NVIDIA Jetson Orin edge device was used for real-time processing.
OpenCV (Python) handled:
A TensorFlow Lite model trained on thousands of defective and non-defective PVC pipes was integrated for enhanced detection accuracy. If a defect was identified, the system:
Defect data was automatically logged in a PostgreSQL database, including:
Grafana dashboards visualized real-time defect trends, helping the operations team identify when the extrusion machine required calibration or maintenance.
• Top-down view for diameter consistency.
• Side view for surface crack detection and cut analysis.
• Crack detection: Using contour analysis and edge detection.
• Diameter measurement: Analyzed by measuring pixel width against a reference standard.
• Cut length validation: Line detection was applied to check for uneven pipe ends and verify consistency with specified measurements.
• Displayed a real-time alert on a Grafana dashboard for the line operator.
• Recorded the defect type and severity score for later analysis.
• Timestamp, defect type, and batch number.
• Measurements for each product (length, diameter consistency).
Technology Stack
Industrial camera
High-speed, high-resolution image capture
NVIDIA Jetson Orin
Edge AI device for real-time analysis.
LED Lighting
For clear visibility on reflective surfaces.
OpenCV (Python)
For image preprocessing and defect detection.
TensorFlow Lite
For lightweight defect detection models.
Python
For pipeline scripting and automation.
Docker
For simplified, containerized deployment.
PostgreSQL
For defect data storage and trend analysis.
Grafana
Real-time dashboard.
Result
This scalable Computer Vision system allowed the PVC manufacturer to automate quality control, reduce material waste, and prevent defective products from reaching customers—all without requiring expensive infrastructure upgrades. By combining edge computing with machine learning models, the client achieved consistent quality assurance while improving operational efficiency.
Key Achievements
Surface cracks and diameter inconsistencies decreased more then twice.
nnual material waste was reduced by $45,000 due to early defect detection.
Achieved 100% automated inspection without slowing production.
Real-time feedback allowed the production team to proactively adjust extrusion settings and avoid mass defects.
