Exploring Transfer Learning Models for Robust Steel Surface Defect Classification: A Comparative Analysis

Abstract

Defect detection in steel surfaces is critical for maintaining product quality and ensuring safety in manufacturing processes. This paper presents a comparative study of various transfer learning models applied to the classification of steel surface defects, including rolled, crazing, pitting, scratches, inclusion, and patches. We evaluate five prominent convolutional neural networks: VGG16, VGG19, MobileNetV2, EfficientNetB0, and ResNet50, which have been pre-trained on large-scale image datasets. The models were fine tuned on a dataset specifically curated for steel surface defects, consisting of diverse images to capture varia tions in appearance and context. Through rigorous experimentation, we assess each model’s performance based on key metrics such as accuracy, precision, recall, and F1 score, as well as their computational efficiency in terms of execution time and memory usage. The findings reveal signifi-cant differences in classification capabilities among the models, highlight ing the strengths and weaknesses of each architecture in the context of defect detection. Our study not only identifies the most effective models for this specific application but also provides insights into the trade-offs between accuracy and resource requirements, offering guidance for prac- titioners in the field of industrial quality control.