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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]

China is gradually transitioning from the "tunnel construction era" to the "tunnel maintenance era," and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value. [ABSTRACT FROM AUTHOR]

Image restoration is a long-standing problem in low-level computer vision. In this paper, we offer a simple but effective estimation paradigm for various image restoration problems. Specifically, we first propose a model-based Gaussian denoising method Adaptive Dual-Domain Filtering (ADDF) by learning the optimal confidence factors which are adjusted adaptively with Gaussian noise standard deviation. In addition, by generalizing this learning approach to Laplace noise, the learning algorithm of the optimum confidence factors in Laplace denoising is presented. Finally, the proposed ADDF is tactfully plugged into the method frameworks of off-the-shelf image deblurring and single image super-resolution (SISR). The approach, coining the name Plug-ADDF, achieves promising performance. Extensive experiments validate that the proposed ADDF for Gaussian and Laplace noise removals indeed results in visual and quantitative improvements over some existing state-of-the-art methods. Moreover, our Plug-ADDF for image deblurring and SISR also demonstrates superior performance objectively and subjectively. [ABSTRACT FROM AUTHOR]