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SUCCESS STORY
The image illustrates a Defect Detection System for a Paper Mill.

AI-driven Defect Detection System for a Leading Paper Mill

ThirdEye Data successfully developed and deployed an AI-driven surface inspection system for a leading paper mill to automate defect detection in paper rolls during high-speed production. The solution leverages computer vision, edge computing, and deep learning to detect wrinkles, tears, and texture issues in real-time—improving production quality, reducing customer claims, and cutting financial losses due to undetected defects.

THE CUSTOMER

The customer is a part of a diversified Large Group of Companies, and the largest newsprint manufacturer and the sole producer of multi-layer coated board in the region. The company is known for manufacturing writing, printing, and packaging papers.

BUSINESS GOALS OR CHALLENGES

Business Goals

  • Automate the surface inspection process using AI to minimize manual errors.
  • Detect surface defects before jumbo roll formation.
  • Reduce compensation payouts from undetected defects.
  • Improve overall product quality and customer satisfaction.
  • Avoid the high cost of replacing legacy systems like PASTEX, ABB, and VALMET.

Understanding the Challenges:

  • Manual inspection allows only a 1-hour window before defects are irreversible.
  • Inconsistent detection due to human error.
  • High compensation costs due to defective deliveries.
  • Existing inspection systems are expensive and inflexible.
  • Limited insights into defect patterns and root causes.

Prerequisites and Preconditions:

The system had to integrate with the high-speed production line, operate in industrial environments, detect a wide variety of defects in real time, and be scalable for future plant-wide deployment.

  • Sample video data of production line defects.
  • Access to the paper production environment for camera installation and testing.
  • Agreement on defect types and classification priorities.
  • Availability of production-specific training data.
  • Hardware deployment feasibility checks (camera placement, lighting, network).

THE SOLUTION

ThirdEye developed a custom AI-based computer vision solution using high-speed industrial cameras, deep learning algorithms, and edge computing devices. The system detects and classifies defects in real-time and generates actionable alerts for operators, minimizing the chances of defective rolls reaching the end customer. We proposed and agreed on a 3-phase implementation—starting with a PoC, followed by pilot deployment and then full-scale rollout.

High-level Solution Approach

  • Camera & Sensor Setup
    Installed high-resolution line-scan cameras with synchronized LED lighting and IR sensors to continuously capture clear images of fast-moving paper rolls.

  • Edge Computing for Real-Time Detection
    Deployed NVIDIA Jetson-powered edge devices on-site to process images locally and detect surface defects in real time with minimal latency.

  • AI Model Development
    Trained deep learning models (CNNs) using thousands of labeled defect images to recognize various paper surface defects like streaks, wrinkles, patches, and more.

  • Automated Alerts & Dashboard
    Built a real-time monitoring dashboard that displays defect type, location, and severity. Integrated alert system notifies operators instantly for quick intervention.

  • System Integration
    Integrated defect data with customer’s existing MES and quality control systems to maintain traceability and streamline reporting.

  • Scalable Architecture
    Designed the system to be modular and scalable, allowing easy expansion to other production lines or addition of new defect types in the future.

Technology Stack:

  • Hardware: Line-scan cameras, LED lighting, infrared sensors, edge GPUs

  • Software: OpenCV, PyTorch/TensorFlow, anomaly detection models, real-time dashboards

  • Platform: Edge computing via NVIDIA Jetson, integrated with existing factory systems

VALUE CREATED

The system has been deployed 3 months back, we are sill in the phase of estimating the final ROI, Here are a few ROI calculations received from the customer based on the last 3 months performance:

  • Around 93% accuracy in detecting multiple surface defect types.
  • Over 36% reduction in product rejections due to early detection.
  • Approximately 25% improvement in overall product quality metrics.
  • 25% reduction in customer returns and rework associated with defective alloy wheels.
  • As per the initial performance, we are expecting ₹1.8 crore annual savings in compensation costs within 7 months of the full-scale deployment
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