Deep Learning-Based Surface Defect Detection in Steel Products Using Convolutional Neural Networks

In mechanical engineering, monitoring steel surface defects is crucial for ensuring the quality of industrial products, as these defects account for over 90% of flaws in steel items. Traditional manual inspection methods are time-consuming and may overlook some defects. To address these challenges, this study introduces an automated deep learning (DL) model for continuous monitoring of steel surface defects using real-world images from the Industrial Machine Tool Component Surface Defect (IMTCSD) dataset, which includes 1,104 three-channel images, 394 of which are categorized as exhibiting "pitting" damage. This study evaluated several Convolutional Neural Network (CNN) classifiers: EfficientNetB3, ResNet-50, and MobileNetV2, to determine the most effective model for defect detection. EfficientNetB3 is distinguished by its scalable architecture that adapts efficiently across various image dimensions, making it ideal for high-accuracy applications on limited computational resources. ResNet-50 uses residual connections to maintain performance in deeper networks by facilitating smooth gradient flow, yet it requires more computational power. MobileNetV2, designed for real-time applications on devices with limited resources, uses lightweight depthwise separable convolutions. The performance of these models was assessed using accuracy, recall, precision, specificity, F1-score, and AUC metrics. EfficientNetB3 emerged as the best performing model, achieving an accuracy of 0.981, specificity of 0.975, recall of 0.987, precision of 0.975, and an F1-score of 0.982. This model proved effective in detecting defects even on dirty surfaces, demonstrating its potential to significantly enhance quality control in industrial settings.