A Review of Deep Learning-Based Steel Surface Defect Detection
DOI:
https://doi.org/10.54097/g36nm962Keywords:
Deep learning technologies; CNN, object detection; steel surface defect detection.Abstract
With the rapid development of deep learning technologies, the detection of steel surface defects has made significant progress, especially in automated industrial production environments. This paper reviews the current state of research on steel surface defect detection, focusing on deep learning-based methods. We discuss traditional and modern techniques, including Convolutional Neural Networks (CNN), YOLO, and other object detection frameworks. We also highlight the challenges faced in this field, such as data dependency, small target detection, multi-scale recognition, and the need for model generalization and industrial deployment. The review emphasizes the necessity of overcoming the limitations of existing models, such as accuracy and efficiency, to achieve real-time, reliable defect detection in complex industrial settings.
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