磁环表面缺陷图像分割算法的研究及实时实现

为实现小型磁环表面细微缺陷图像无监督分割,并提高分割精度与计算效率,本文提出了一种基于改进2D Gabor滤波器组的自适应阈值分割方法。首先,利用多尺度、多方向的Gabor滤波器组对缺陷图像进行滤波降噪处理,抑制目标区域与背景区域内部的噪声污染,同时增强区域间的差异性;然后,通过对处理后图像的灰度统计特性分析,根据缺陷图像的灰度均值及方差构造了灰度阈值计算公式,实现了小型磁环表面细微缺陷图像的自适应分割。实验结果表明,本文算法可快速、准确地分割缺陷并抑制噪声干扰,在分割精度、计算效率等方面也优于传统的选择迭代法、OTSU、最大熵等方法,并能够在先进的SEED-DVS8168平台上实时实现,验证了此算法的可行性与实时性。

Research on segmentation algorithm for magnetic rings surface defect image and real-time implementation

In order to realize the unsupervised segmentation of small magnetic ring surface micro-defect image and improve the segmentation accuracy and computational efficiency,an adaptive threshold segmentation method based on an improved 2D Gabor filter bank is proposed.Firstly,a multi-scale and multi-direction Gabor filter is used to filter and de-noising the defect image,so as to suppress noise pollution within the target region and background region and enhance the difference between areas.Then,through the analysis of the statistical characteristics of the processed the gray of the image,based on the gray mean value and variance of the defect image,the gray threshold calculation formula is constructed,and an adaptive segmentation of the small magnetic ring surface fine defects images was realized.The experimental results show that the proposed algorithm segments the defects and reduces the noise accurately.It outperforms the traditional methods in terms of segmentation accuracy and computational efficiency,such as selective iterative,OTSU,maximum entropy and other methods,and it can be implemented in real time on the advanced SEED-DVS8168 platform,verifying the feasibility and real-time performance of this algorithm.