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Computer Vision-based Object Counting

Applicable Industries

  • Electronics
  • Manufacturing
  • Automotive
  • Retail & Inventory Management 
  • Textile & Apparel
  • Food & Beverage
  • Healthcare
  • Supply Chain & Logistics

Technologies Used & Their Role

  • Object Detection & Counting: YOLOv8, OpenCV
  • AI Model Training: TensorFlow
  • Image Processing & Optimization: OpenCV
  • Real-time Edge Deployment: Edge AI
  • API & System Integration:
    FastAPI

Summary of the AI Solution - Why We Developed This Solution

Manufacturers dealing with small components often struggle with accurate counting during production and quality control. Manual counting is error-prone, leading to defects, rework, and operational inefficiencies. 

This AI-powered Object Counting System automates counting using computer vision, ensuring high accuracy, real-time processing, and seamless integration with manufacturing workflows. 

Problem Statement

A U.S.-based electronic connector manufacturer needed a high-precision automated counting system for: 

  • Counting wires in bundles or sockets – Manual counting introduced inconsistencies and quality control issues. 
  • Reducing human errors – Variability in manual inspections led to inaccurate counts. 
  • Ensuring real-time tracking – The company required an automated, scalable solution that could integrate with existing production lines. 

A computer vision-based system was needed to automate object counting, improving efficiency and accuracy. 

Request a Demo to Watch It Live in Action and Try It on Your Datasets.

Solution Approach

To address these challenges, we developed a Computer Vision-based Object Counting System with the following approach: 

  1. Object Detection & Classification
    –     Used YOLOv8 for real-time object detection and counting.
    – Applied OpenCV for image preprocessing and enhanced feature extraction. 
  2. AI Model Training & Optimization
    –     Trained TensorFlow-based deep learning models on labeled datasets of wires and small components.
    – Optimized for accuracy, speed, and adaptability to different lighting and positioning conditions. 
  3. Edge AI Deployment for Real-time Processing
    –     Implemented Edge AI deployment to process images locally on manufacturing hardware, reducing latency.
    – Ensured the system runs efficiently on industrial cameras and embedded devices. 
  4. Seamless API-based Integration
    –     Built a FastAPI-based lightweight API to connect with Manufacturing Execution Systems (MES).
    – Enabled automated reporting and real-time monitoring of counting results. 

Key Benefits & Value Proposition

  • 99% Accuracy in Object Counting – Reduces human errors and miscounts.
  • Real-time Detection & Reporting – Ensures instant feedback on counting errors.
  • Automated Quality Control – Flags miscounts and missing components in real time. 
  • Seamless Integration with Existing Systems – Connects with CRMs, MES and ERPs platforms. 
  • Scalable & Adaptable – Works across various industries and component types.

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