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Pitting corrosion can lead to critical failures of infrastructure elements. Therefore, accurate detection of corroded areas is crucial during the phase of structural health monitoring. This study aims at developing a computer vision and data-driven method for automatic detection of pitting corrosion. The proposed method is an integration of the history-based adaptive differential evolution with linear population size reduction (LSHADE), image processing techniques, and the support vector machine (SVM). The implementation of the LSHADE metaheuristic in this research is multifold. This optimization algorithm is employed in the task of multilevel image thresholding to extract regions of interest from the metal surface. Image texture analysis methods of statistical measurements of color channels, gray-level co-occurrence matrix, and local binary pattern are used to compute numerical features subsequently employed by the SVM-based pattern recognition phase. In addition, the LSHADE metaheuristic is also used to optimize the hyperparameters of the machine-learning approach. Experimental results supported by statistical test points out that the newly developed approach can attain a good predictive result with classification accurate rate = 91.80%, precision = 0.91, recall = 0.94, negative predictive value = 0.93, and F1 score = 0.92. Thus, the newly developed method can be a promising tool to be used in a periodic structural health survey. [ABSTRACT FROM AUTHOR]