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

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. © 2017, Science Press. All right reserved.