A Review of Metal Surface Defect Detection Technologies in Industrial Applications
Objective
A Review of Metal Surface Defect Detection Technologies in Industrial Applications The main objectives of this review are to examine current technologies used for detecting defects on metal surfaces in industrial applications.It highlights various image processing and machine learning approaches used for identifying cracks, dents, scratches, and other imperfections.The review also compares detection methods in terms of accuracy, speed, cost-effectiveness, and suitability for real-time inspection systems.
Abstract
Metal surface defect detection plays a vital role in ensuring product quality and reliability in industrial manufacturing. This review presents a vision-based inspection prototype for automated detection and classification of metal surface defects using deep learning techniques. The system is developed using a Raspberry Pi as the central controller, supported by a memory card for data storage and processing. A USB web camera captures real-time images of metal surfaces during inspection. A YOLOv8 (You Only Look Once version 8 deep learning model is deployed on the Raspberry Pi to identify and classify common defects such as rolled_pit, waist folding, welding_line, and crease. The detected results are displayed on an LCD screen, and a buzzer is activated when a defect is detected to provide immediate alert notification. The hardware components are interconnected using reliable connectors, and the system operates with a standard 5V Type-C adapter. The proposed prototype offers a compact, cost-effective, and real-time automated inspection solution suitable for industrial environments, minimizing manual errors, improving inspection accuracy, and enhancing overall production efficiency.
Keywords:Metal Surface Defect Detection, Deep Learning, YOLOv8,Raspberry Pi, Automated Industrial Inspection.
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Specifications
Hardware components:
- Raspberry pi
- Memory card
- Usb web camera
- Lcd
- Buzzer
- Connectors-10.
Software requirements:
- Raspbian os
- Python
Paper Publishing
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