Advancing Quality Control in Manufacturing with AI-Powered Computer Vision

Client Overview

Our client, a well-established manufacturing company in Mexico, specializes in assembly line production across a range of industrial components. To streamline their quality control (QC) processes, they sought a proof of concept (PoC) for a computer vision system powered by deep learning, integrated with sensors and cameras. The goal: automate QC operations for higher precision and efficiency.

Business Challenge

In the rapidly evolving landscape of Industry 4.0, manual quality control processes were becoming increasingly inefficient and error-prone. The client faced multiple challenges:

• High dependency on human inspectors leading to subjective quality assessments
• Inconsistent quality control across different shifts
• Growing production volumes requiring faster inspection cycles
• Need for real-time defect detection and reporting
• Increasing pressure to maintain competitive edge in the market

The client wanted an intelligent, automated QC system that could identify, classify, and assess components in real time with accuracy. This system would be designed to tackle multiple QC tasks, including:

1. Product Classification: Developing classifiers to categorize products as “pass” or “fail” based on an image dataset, such as distinguishing between compliant and defective connectors.
2. Localization and Identification: Precisely locating and identifying components within an image frame, such as detecting the positioning of parts on an electric circuit board.
3. Object Detection and Counting: Accurately detecting and counting items within a frame to verify component assembly.
4. Defect Detection: Identifying surface-level or structural defects (e.g., cracks, contamination, burrs) in real-time.
5. Segmentation: Segmenting distinct parts of components or machinery for targeted QC analysis, such as isolating assembly areas on circuit boards.
6. Optical Character Recognition (OCR): Reading and extracting serial codes or part numbers from images for traceability and verification.
7. User Interface (UI): Building an intuitive UI for operators to interact with the vision modules efficiently.

The client’s vision for the PoC was clear: an agile, high-precision QC system that minimized human error, maximized operational throughput, and integrated seamlessly into their existing infrastructure.

Expected outcome:

TotemXLabs was engaged to architect a solution capable of delivering:

• Development of modular vision system components for different QC tasks
• Achievement of >95% accuracy in defect detection and classification
• Real-time processing capabilities with minimal latency
• User-friendly interface for operators
• Scalable architecture for future expansion
• Comprehensive documentation and training materials
• ROI analysis for full-scale implementation

Our Approach

Drawing from our expertise in computer vision and industrial AI, we implemented a structured, phased approach from discovery through to deployment. Each module was carefully engineered to address specific QC requirements, ensuring seamless integration and operational scalability.

Discovery phase

Our discovery phase laid the groundwork for a custom solution tailored to the client’s QC challenges and business objectives. Through intensive stakeholder knowledge sharing and  data assessments, we clarified:

• Technical Requirements and KPIs: Defined metrics and success criteria for each vision module, including performance benchmarks for accuracy and processing speed.
• Resource and Data Needs: Scoped resources for data preparation, hardware requirements, and cloud infrastructure considerations.
• Risk Analysis and Mitigation Planning: Identified potential obstacles (e.g., data scarcity, environmental conditions) and outlined contingency strategies to address them proactively.

This early insight proved crucial in our hardware specifications and algorithm design. Our collaborative approach enabled us to align project milestones with the client’s operational cadence, ensuring a solution design that met both technical and business requirements.

Data

If AI is the engine of modern manufacturing, data is its premium fuel. Our data journey began with a sobering reality check – while the client had thousands of products passing through their lines daily, they had fewer than 100 labeled images per defect category. This was like trying to teach someone to recognize faces by showing them just three photos.

Our data strategy involved:

• Data collection and curation from production lines
• Implementation of data augmentation techniques to address limited dataset sizes
• Development of custom annotation pipelines for specialized components
• Creation of data augmentation workflows
• Establishment of data validation and verification protocols

Given the stringent accuracy demands, our data engineering phases prioritized high-quality data processing for each computer vision module robustness:

1. Image Classification: To overcome the client’s limited dataset, we deployed data augmentation and synthesis techniques, simulating additional scenarios to increase data volume and diversity. 
2. Localization, Detection, and Segmentation: These modules required extensive annotated data. Our team utilized a hybrid approach of automated and manual annotations, achieving pixel-level accuracy in labeling.
3. Optical Character Recognition (OCR): Traditional OCR approaches proved insufficient for the client’s assembly conditions, given issues such as poor lighting, curved surfaces, and reflective materials. By customizing our OCR annotation pipeline with pre-processing steps. 

These data-centric optimizations allowed us to deliver each module with the accuracy, flexibility, and scalability required by the client’s production environment.

AI Solution Design and Development

Having laid a solid foundation with quality data, we moved into the solution design phase with the precision of a Swiss watchmaker. Our architecture needed to handle seven distinct vision tasks, each with its own unique challenges, while maintaining real-time performance – think of it as juggling seven balls while riding a unicycle. Our solution architecture comprised seven distinct vision modules:

1. Classification Module
   • Purpose: Categorize client’s vast component portfolio products/components into predefined classes
   • Implementation: Custom CNN architecture with transfer learning
   • Achievement: 99% accuracy on test datasets
2. Localization Module
   • Purpose: Precise component positioning in frame
   • Implementation: YOLO/SSD/Mask R-CNN/MMDetection – based custom implementations
   • Achievement: Sub-pixel accuracy in component localization
3. Object Detection & Counting
   • Purpose: Automated component counting and verification
   • Implementation: YOLO/SSD/Mask R-CNN/MMDetection-based architecture with custom modifications
   • Achievement: Real-time detection with 98% accuracy
4. Defect Detection
   • Purpose: Identify manufacturing defects (cracks, contamination, burrs)
   • Implementation: Ensemble of specialized detection models
   • Achievement: 97% defect detection rate
5. Segmentation Module
   • Purpose: segment complex objects, ensuring reliable performance even under varying lighting and positioning conditions
   • Implementation: U-Net with varying combination (of backbones, necks and heads) architectures with domain adaptations
   • Achievement: 95% IoU score on test cases
6. OCR Module
   • Purpose: Text extraction from components
   • Implementation: Custom OCR pipeline for challenging industrial conditions
   • Achievement: 94% accuracy in varied lighting conditions
7. Unified UI Interface
   • Purpose: Intuitive operator interface
   • Implementation: Web-based dashboard with real-time visualization of vision module outcomes
   • Achievement: < 500 ms response time

Deployment

We containerized our solution using Docker, ensuring consistent performance across different hardware configurations. Post-deployment monitoring revealed something remarkable: not only was our system performing vision tasks at SOTA accuracy, it was identifying subtle quality patterns that even experienced inspectors would have missed. We had built not just an automation tool, but a quality insight engine. Throughout development, we ensured full transparency with the client via extensive documentation and periodic progress updates. Regular touchpoints, including weekly and bi-weekly check-ins, allowed us to address issues promptly and align on iterative refinements. Our agile project management framework ensured that all milestones were met on schedule, with deliverables rigorously tested and benchmarked against client KPIs.

Client Testimonial

Why Choose TotemXLabs

TotemXLabs is more than a technology partner—we’re your competitive edge in the era of Industry 4.0. Our end-to-end solutions are built on a foundation of agile development, rigorous data science, and high-caliber machine learning engineering. From edge-to-cloud integrations to bespoke AI modules, we’re equipped to tackle the nuances of modern manufacturing and beyond.

Why settle for less when you can have a custom, future-ready solution? Let TotemXLabs bring the power of advanced AI and computer vision to your production line. Connect with us today to redefine what quality control can achieve.