Abstract: In the process of 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 FCM clustering algorithms do not consider spatial neighborhood information and are sensitive to noise interference, resulting in poor segmentation accuracy; And overall, it is susceptible to the influence of initial values, leading to a slower convergence speed. To address these issues, an improved FCM algorithm is proposed in this paper, which replaces Euclidean distance with simple two terms of kernel induced distance , map the original spatial pixels to the high-dimensional feature space to increase the linear separability probability and computation speed; By utilizing the spatial correlation between image pixels and introducing an improved Markov random field to modify the FCM objective function, the algorithm's noise resistance and segmentation accuracy are improved; Using the Bald Eagle Search Algorithm to determine the initial clustering center of FCM, improves detection accuracy and convergence speed, at the same time, it also avoids the situation where the algorithm is prone to falling into local extremum. To verify the performance of the improved FCM algorithm, partition entropy, partition coefficient, Xie_Beni coefficient, and iteration number were used as evaluation indicators, and compared with image segmentation algorithms proposed by different scholars in recent years through experiments, the effectiveness of the algorithm was verified. The experimental results show that the algorithm proposed in this paper has good noise resistance and can achieve good defect segmentation results, which has a certain degree of application value for defect detection of stamping parts in industry.

摘要： 工业质检过程中，冲压件缺陷图像分割作为缺陷检测的重要环节，直接影响缺陷检测效果。而传统的FCM聚类算法未考虑到空间邻域信息，对于噪声干扰较为敏感，导致分割精度较差；且整体易受初始值的影响，导致收敛速度变慢。针对上述问题，本文提出一种改进的FCM算法，采用内核诱导距离中的简单两项代替传统的欧氏距离，将原有的空间像素映射到高维特征空间，提高线性可分概率和计算速度；利用图像像素之间的空间相关性，通过引入改进的马尔可夫随机场对FCM目标函数进行修正，提高了算法的抗噪能力以及分割精度；采用秃鹰搜索算法确定FCM的初始聚类中心，提高了算法的收敛速度，同时也避免了算法易陷入局部极值的情况。为验证改进的FCM算法性能，选取划分熵、划分系数、Xie_Beni系数以及迭代次数作为评价指标，并与近年来不同学者提出的图像分割算法进行实验对比，验证了算法的有效性。实验结果表明，本文算法具有较好的抗噪能力，能得到较好的缺陷分割效果，对工业上冲压件的缺陷检测有一定程度的应用价值。