| Summary: | During industrial quality inspection, image segmentation of stamping defects is an important part of defect detection, which directly affects the effectiveness of defect detection. However, traditional Fuzzy C-Means (FCM) clustering algorithms overlook spatial neighborhood information and are sensitive to noise interference, resulting in poor segmentation accuracy. Furthermore, they are susceptible to the influence of initial values, which leads to a slower convergence speed. To address these issues, this study proposes an improved FCM algorithm, which replaces Euclidean distance with simple two terms of kernel-induced distance, and maps the original spatial pixels to the high-dimensional feature space to increase the linear separability probability and computation speed. The algorithm improves noise resistance and segmentation accuracy by utilizing the spatial correlation between image pixels and introducing an improved Markov random field to modify the FCM objective function. Using the Bald Eagle Search (BES) algorithm to determine the initial clustering center of FCM improves detection accuracy and convergence speed. Simultaneously, it also avoids the situation where the algorithm is prone to falling into local extremum. To validate the performance of the improved FCM algorithm, partition entropy, partition coefficient, Xie_Beni coefficient, and iteration number are used as evaluation indicators and compared with image segmentation algorithms proposed by different scholars in recent years through experiments. Experimental results show that the algorithm proposed in this paper has good noise resistance and can achieve good defect segmentation results, which implies a certain degree of application value for defect detection of stamping parts in the industry.
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