New Class Discovery of Steel Surface Defects Using Multi-View Self-Labeling and Overclustering

In this paper, we study the problem of discovering new types of defects on steel surfaces. Steel is an indispensable and important material in modern industry, making steel surface inspection critically important. However, existing methods are insufficient for discovering new types of steel surface defects. A method based on UNified Objective function (UNO) was developed to address these limitations. UNO unifies all objectives under a single cross-entropy loss using multi-view self-labeling, which simplifies the integration of supervised and unsupervised learning. Additionally, multi-head clustering and overclustering strategies are integrated to improve clustering performance and representation quality. Experimental validation demonstrates that our method significantly outperforms existing methods on the NEU dataset, achieving an Adjusted Rand index of 0.9750 and a Normalized Mutual Information of 0.9614.