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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:

Object Detection & Classification
– Used YOLOv8 for real-time object detection and counting.
– Applied OpenCV for image preprocessing and enhanced feature extraction.
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.
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.
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.

Computer Vision-based Object Counting

Applicable Industries

Technologies Used & Their Role

Summary of the AI Solution - Why We Developed This Solution

Problem Statement

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

Solution Approach

Key Benefits & Value Proposition

Primary Services

Pre-Built Applications

Data & AI Solutions

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Insights

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