A DAG-SVM classifier based method for identifying surface defects in  strip steel

Wenwen Xu; Juanjuan Gu

doi:10.1117/12.2680750

23 May 2023 A DAG-SVM classifier based method for identifying surface defects in strip steel

Wenwen Xu, Juanjuan Gu

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452X (2023) https://doi.org/10.1117/12.2680750
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

In recent years, the requirements for strip steel surface quality have also increased. However, traditional Non-destructive testing detection is not good enough. Deep learning-based algorithms are also unable to meet the requirements of small sample data and real-time performance for strip surface defect detection. The DAG-SVM has the advantage of solving small data, non-linear and high-dimensional pattern recognition. Therefore, this paper selects the Directed Acyclic Graph (DAG) to construct the SVM multi-class classifier. And representative shape features are selected as the feature vectors of the samples. At the same time, the kernel function and parameter search are used to further improve the recognition rate. The final results show that the average recognition rate using this method is 95.5%, which can meet the practical needs. In the comparison with BP neural network, the method of this paper is also better than BP neural network in general.

Citation Download Citation

Wenwen Xu and Juanjuan Gu "A DAG-SVM classifier based method for identifying surface defects in strip steel", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452X (23 May 2023); https://doi.org/10.1117/12.2680750

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