Steel Surface Defect Detection
Objective
The primary objective of this project is to develop an automated steel surface defect detection system that accurately identifies six distinct types of defects using the YOLOv8 model. Specific objectives include enhancing defect detection speed and precision to facilitate real-time applications, reducing the dependency on manual inspection to improve operational efficiency, and minimizing human error in quality control processes
Abstract
The "Steel Surface Defect Detection" project leverages YOLOv8 for real-time, high-accuracy detection of six steel defect types: crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches. The system is developed using Python for backend operations, with Flask as the framework, while HTML, CSS, and JavaScript are used for a responsive frontend interface. By harnessing YOLOv8's object detection capabilities, the system identifies and classifies surface defects on steel, facilitating quality control and defect management in industrial processes. The integration of Flask enables a seamless user experience, allowing users to interact with the system to upload images and visualize defect detection results. This solution aims to optimize production quality and reduce waste by ensuring defect-free products reach the market.
Keywords: Steel defect detection, YOLOv8, Flask framework, real-time classification, surface quality controlNOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware Requirements
Operating system : Windows 7 or 7+
RAM : 8 GB
Hard disc or SSD : More than 500 GB
Processor : Intel 3rd generation or high or Ryzen with 8 GB Ram
Software Requirements:
Software’s : Python 3.10 or high version
IDE : Visual Studio Code.
Framework : Flask
IDE/Workbench : PyCharm
Technology : Python 3.6+
Server Deployment : Xampp Server
Database : MySQL
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