Automatic visual defect detection in industrial products using deep learning methods

This repository contains our team's (NTUA-INDUSTRY) project for the competition Industry 4.0 hosted by crowdpolicy

🏭 Project Description

Problem Statement

• Quantity and quality automation in manufacturing is a demand not a privilege. In this direction, our team proposed a system to resolve the material quality control problem. Human Inspection is an error prone & time consuming process and results in false positives

  • Parts corectly detected as faulty provoke loss in upstream labor, consumables as well as revenue
  • Parts wrongly detected as faulty provoke a market reaction and eventually a unrepairable damage to company's reputation

• In that direction every automation system that can early detect false positives without human intervation, while being cost-effective for the customer, is preffered.

Our solution

• We automated defect detection using deep learning, and more specicially convolutional neural networks. The model is trained on steel images, since it is the most important building material of modern times.

📋Contents

📙Datasets

⚙️ Tools

👱Project Collaborators