The quality control of steel products' surface is of utmost importance, where several inspection techniques and technologies have been proposed over the last few years. Traditional manual inspection procedures face several limitations and often fall short in ensuring flawlessness. Vision-based strategies for automatic steel surface inspection have emerged as powerful and effective tools to solve various industrial-related problems, including product quality control. Therefore, the current study aims to improve the recognition rate of steel surface defects classification systems by introducing a novel classifier combination approach. The proposed system utilizes two distinct feature sets, namely LCCMSP and DCP, which were carefully selected based on a comprehensive comparative study of 19 state-of-the-art texture descriptors, considering both accuracy and time consumption. These generated features are individually fed to two classifiers, SVM and RF, leading to the creation of four base classifiers. In the final step, the Bayesian fusion rule is applied to integrate the outputs of these classifiers, ultimately providing the definitive classification decision. To evaluate the proposed system, two steel surface defects datasets, NEU-CLS and X-SDD, are utilized. The experimental results demonstrate that the proposed combination approach surpasses classical combination methods achieving remarkable outcomes compared to existing steel surface defects classification approaches. This highlights the effectiveness and superiority of the proposed system in accurately identifying and classifying steel surface defects while maintaining fast execution time.
