Résumé:
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
