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.

Organizations around the world trust us

Hear from

our clients

Hear from our clients

Lee Ebreo

VP of Engineering at Credit Ninja

These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.

Selah Ben-Haim

VP of Engineering at Prominence Advisors

Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).

Ben Crawford

Senior Product Manager at Enova Financial

I would expect to be delighted. It's been a really positive experience, working with Sphere, and I would expect you to have the same.

Mark Friedgan

CEO at CreditNinja

Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. As an offshore team, they have been an integral part of our organization and we plan to continue growing with them.

René Pfitzner

Co-Founder at Experify

Sphere provided excellent full-stack development manpower to augment our team and help push our product forward. They are easy to work with, tech-savvy and proactive.

Bruce Burdick

Chief Information Officer at Integra Credit

We've been working with Sphere and its excellent consultants since our founding. I've found that they are true partners in the success of our business.

Jemal Swoboda

CEO at Dabble

The resources and developers that Sphere Software provides are skilled and have the required technical expertise, but more importantly, they have helped us build a culture of excellence within our team.

Arthur Tretyak

Founder and CEO at IntegraCredit

With Sphere, we were able to migrate in half the time it would take to train an additional FTE… and for a fraction of the cost. Our experience with Sphere has been exceptional.

Lee Ebreo

VP of Engineering at Credit Ninja

These things would not have been achievable if we did not build our own in-house system and if we did not partner with Sphere to help us achieve our goals.

Selah Ben-Haim

VP of Engineering at Prominence Advisors