针对匣钵底面裂纹图像背景复杂,裂纹分布密集、断续严重,裂纹特征不明显,从而导致裂纹提取难度大的问题,提出了一种基于扇形邻域差异直方图的匣钵裂纹检测方法。根据裂纹像素点的空间聚集特征和方向特征,构造多尺度、多方向扇形滤波器;通过计算扇形滤波器与图像卷积的结果,获取能够反映裂纹分布概率特征的扇形邻域差异直方图;提取裂纹分布概率特征,并根据裂纹像素点和非裂纹像素点在该特征上的差异,实现裂纹提取;最后,提出基于裂纹全局及局部的长度和分布面积特征融合的指标,对匣钵龟裂程度进行评估。实验结果表明,该算法对匣钵底面上各种类型的裂纹都能实现良好的提取效果,正确率和召回率均可达到90%以上,优于现有其他较好的裂纹提取方法,龟裂程度评估方法的评估结果也与人的主观评估结果一致。

The image background of the crack on the sagger bottom is complicated, the distribution of cracks is dense and intermittent, and characteristics of cracks are not obvious, so crack extraction of the sagger bottom is difficult. To solve the problems, a method for detecting sagger cracks based on sector neighborhood difference histogram is proposed. A multi-scale, multi-direction sector filter is constructed according to the spatial clustering characteristic and directional characteristic of the crack pixels. By calculating the convolution of the filters with the image, a sector neighborhood difference histogram that can reflect the crack distribution probability feature is obtained. Crack extraction is realized depending on the difference in crack distribution probability characteristics between the crack pixels and non-crack pixels. Finally, the global and local length and distribution area characteristics of the cracks are integrated to evaluate the degree of cracking. The experimental results show that the proposed algorithm can achieve good extraction results for all types of cracks on the sagger bottom. The precision and recall of the algorithm can reach higher than 90%, which is better than some of the existing good methods for crack extraction. The assessment results of the method for assessing the severity of cracks are also basically the same as those of a person's subjective assessment.