全局阈值自适应的高亮金属表面缺陷识别新方法

Novel defect recognition method based on adaptive global threshold for highlight metal surface

高反射类零部件在其生产及后期处理过程中,可能会产生划痕、擦伤等表面缺陷,严重影响产品的使用性能和寿命。该类零件表面具有镜面反光特性,易导致检测过程中缺陷目标的漏检、错检。针对这类问题,基于数字图像处理技术,提出一种具备全局阈值自适应调整的高亮表面缺陷识别新方法。首先,构造利用空域和值域信息的滤波方式对原始图像进行处理,保护目标边缘信息;其次,以高斯函数的一阶导数构建Canny最优边缘检测器,结合全局阈值最大类间方差法和形态学图像分割法,完成图像分割以及相应阈值的自适应调整,实现对缺陷目标的识别。实验结果验证了算法的有效性及可靠性,能够在排除高光影响的基础上有效地识别缺陷目标,对高亮金属表面缺陷识别具有重要意义。

Various surface defects such as scratch, scrape and etc. may occur on the high reflective metal parts with highlight surface during production and post-treatment, which seriously affects the performance and service life of the product. The surface of this kind of parts has the characteristic of specular reflection, which leads to the miss detection and wrong detection of the defect object in detection process. Aiming at this problem, this paper proposes a new highlight surface defect recognition method with global threshold adaptive adjustment capability based on digital image processing technology. Firstly the filtering pattern fully using the information of both spatial domain and value domain is constructed, which is used to process the original image and preserve the edge information of the object. Secondly, the first derivative of Gaussian function is used to construct the Canny optimal edge detector, which combines with the global threshold maximum between-class variance method （Otsu segmentation method ） and morphological image segmentation method to complete the image segmentation and the adaptive adjustment of corresponding threshold, and achieve the identification of the defect object. Experiment results verify the effectiveness and reliability of the algorithm. The proposed algorithm could effectively identify the defect object while eliminating the influence of highlight interference. The method has great significance to the automatic and accurate defect recognition on the highlight surface of the metal parts.