Application of deep learning with the one-vs-all approach for the task of multiclass classification of metal surface defects

The article investigates the problem of multiclass classification of metal surface defects using deep learning methods. The primary approach employs a "one-vs-all" strategy, which effectively separates different defect classes. Initial analysis utilized the NEU dataset, comprising six defect categories. The resulting metrics were compared against existing solutions, after which the dataset was extended with an additional class from the "Severstal: Steel Defect Detection" dataset. Two convolutional neural network architectures were proposed, each tailored to the respective set of classes. The first architecture consists of five convolutional layers, five max-pooling layers, and two fully connected layers. The second architecture includes two additional layers: an extra convolutional layer and an additional max-pooling layer. Evaluation on the NEU dataset demonstrated high performance: the final model achieved an accuracy of 98.33 %, precision of 98.39 %, recall of 98.33 %, and an F1-score of 98.33 %. Analysis of the results showed that the proposed approach achieves performance comparable to other research results, and the proposed architectures are on par with state-of-the-art solutions. The model also exhibits good processing speed – up to 103 frames per second on the CPU – making it suitable for industrial deployment and enabling real-time defect detection. After extending the solution with the additional class, the model maintained strong performance, achieving an accuracy of 97.14 %, precision of 97.24 %, recall of 97.14 %, and an F1-score of 97.12 %, which suggests robustness and scalability of the proposed solution based on the "one-vs-all" approach.

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