An Efficient Optimization Approach for Steel Surface Flaw Classification using Machine Learning

Abstract

Automatic steel surface defect classification is a well-known problem and is being considered for more than two decades. On the other hand, manual methods for defect classification in steel products are time consuming, labor intensive, and are prone to error. As a result, a real-time surface defect classification method using genetic algorithm is proposed to classify four kinds of surface defects in steel surfaces such as Roller Mark, Rust Spot, Emulsion Spot and Scrape. The proposed inspection system consists of visual dataset of 500 steel defect images for processing. The overview of the classification model has online and offline process. In offline, the input image is pre-processed and the following visual features such as geometric features, shape features and texture features are extracted from the defect image. In order to optimize the extracted visual features and to improve the accuracy of the classification model, genetic algorithm is used to find an optimal solution. For optimization, an initial population is generated randomly and the fitness function is calculated for every chromosome. Then the genetic operations such as selection, crossover and mutation are done to generate new offspring chromosome and evaluated to pick optimal chromosome or optimal feature. Finally, in online, the selected features are given as input to the Optimized SVM classifier, in order to predict the class of defect to which the input image belongs. Accuracy, sensitivity, specificity, f-measure and precision are used as the performance metrics to evaluate the system.

Introduction

Image Processing Overview

Image processing transforms images into digital form to enhance them or extract useful information. It involves operations on a 2D array of pixels, each having a value and location. Important types of image features include:

• Geometric Features: Shapes defined by points, lines, and curves.

• Shape Features: Attributes like rectangularity and density that describe object form.

• Texture Features: Describe spatial arrangement of pixel intensities; extracted using techniques like LBP, GLCM, and Tamura.

2. Image Classification

Classifies pixels or regions into categories using image features. There are two main types:

• Supervised Classification: Uses labeled training data to guide the classification.

• Unsupervised Classification: Groups pixels based on inherent patterns without pre-labeled training data.

3. Genetic Algorithm (GA) Basics

A heuristic optimization technique inspired by natural evolution, useful in feature selection and classifier optimization.

• Fitness Function: Measures the quality of a solution.

• Encoding: Represents chromosomes using binary strings or other structures.

• Operators:

  • Selection: Chooses parents for reproduction.

  • Crossover: Combines two parents to produce offspring.

  • Mutation: Randomly alters genes to introduce diversity.

• Termination: Stops when a condition like max generations or optimal fitness is met.

4. Application Need

In steel manufacturing, surface defect detection is crucial for improving product quality and safety. Automating defect classification reduces human involvement in hazardous environments.

5. Literature Review

Various methods and classifiers (SVM, binary classifiers, fuzzy systems) have been explored:

• SVM is widely used due to its accuracy and generalization ability.

• Genetic Algorithms are applied to enhance classification by optimizing feature selection.

• Hybrid approaches (e.g., combining wavelet/Gabor transforms with GA or SVM) have shown promising results for efficient and precise image classification.

6. Proposed Method

The paper proposes an Optimized SVM classification model enhanced by Genetic Algorithm for steel defect classification. It includes:

Architecture:

• Offline Module:

  • Input image is pre-processed (noise removal, illumination correction, binarization).

  • Feature Extraction:

    • Geometric: Area, centroid.

    • Shape: Rectangularity, density.

    • Texture: LBP, GLCM, Tamura.

  • Feature Optimization: GA is used to select the best features for classification.

  • Optimized SVM: Trained with selected features.

• Online Module:

  • Uses the optimized SVM to classify new defect images.

7. Methodology Steps

1. Image Pre-processing:

   • Convert to grayscale, remove noise, enhance contrast, binarize image.

2. Feature Extraction:

   • Compute geometric (area, centroid), shape (rectangularity, density), and texture features (LBP, GLCM, Tamura).

3. Feature Selection:

   • Genetic algorithm optimizes which features are most relevant.

4. Classification:

   • Multi-class SVM is trained and tested with the optimized features.

Conclusion

The proposed system is to classify four kinds of surface defects in steel surfaces such as Roller Mark, Rust Spot, Emulsion Spot and Scrape. In offline, the input image is pre-processed and the following visual features such as geometric features, shape features and texture features are extracted from the defect image. In order to optimize the extracted visual features and to improve the accuracy of the classification model, genetic algorithm is used to find an optimal solution.For optimization, an initial population is generated randomly and the fitness function is calculated for every chromosome. Then the genetic operations such as selection, crossover and mutation are done to generate new offspring chromosome and evaluated to pick optimal chromosome or optimal feature. Finally, in online, the selected features are given as input to the Optimized SVM classifier, in order to predict the class of defect to which the input image belongs. Accuracy, sensitivity, specificity, f-measure and precision are used as the performance metrics to evaluate the system. The accuracy of the proposed system is reported by SVM as 78%. Thus, the proposed system provides an automatic surface inspection of steel surfaces and it can be employed in steel firms. As a future work, the research will focus on other shapes for more effective defect prediction and to improve accuracy.

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Copyright

Copyright © 2025 Dr. Edison Prabhu K, Godlin Debby K J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.