The primary goal of this study is to enhance surface defect detection in steel manufacturing through advanced machine learning techniques. Traditional inspection methods often fall short in terms of ...

At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery ...

Abstract: Due to the fast development of the use of solar energy, it is necessary to efficiently maintain and manage the waste of solar panels. Solar energy has been a source of sustainable and widely ...

Effectively detecting subtle surface defects in strip steel is vital for industrial quality assurance; however, most existing approaches fail to strike an optimal balance between accuracy and ...

Steel surface defect detection constitutes a critical inspection task in industrial production. To address challenges including missed detections and low accuracy for fine defects, this study develops ...

This research investigates deep learning-based approach for defect detection in the steel production using Severstal steel dataset. The developed system integrates DenseNet121 for classification and ...

In recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to ...

The stable operation of a power supply system is inseparable from the work of detecting defects in transmission lines. However, the insulator defect detection model based on deep learning is widely ...

Abstract: Reusing glass bottles is one of the many ways to help reduce pollution and waste, and detecting defects is an important part of the glass bottle reusing sector to prevent damaged products ...

Using deep learning-based methods to detect surface defects in strip steel can reduce the impact of human factors and lower costs while maintaining accuracy and efficiency. However, the main ...