铁道标准设计2017，Vol.61Issue(5)：50-53,57,5.DOI:10.13238/j.issn.1004-2954.2017.05.011
基于机器视觉的钢轨表面检测光学模型的研究
Study on Optical Model for Rail Surface Detection Based on Machine Vision
摘要
Abstract
In the rail surface defect detection, images are the most important and original data of the entire system, their quality determines the effectiveness and speed of post-image processing.To investigate the gathering of high-quality images, this paper proposes a optical theory model based on the linear CCD camera and the linear light source to detect the defects on rail surface, analyze the causes of the vibration vague in the linear CCD system, deduce the relationship between the amplitude of system, the depth of the defect and the image gray level and to study the influence of the illumination angle and the camera angle on the image gray level and the contrast of defect area.The rationality of the proposed model is proved by experiment.The results show that the image gray level of the defect area decreases with the increasing of the defect depth and a lower illumination angle can highlight the contrast between defects and backgrounds, which facilitates the image identification in late image processing.关键词
钢轨检测/机器视觉/光学模型/高质量图像/缺陷对比度Key words
Rail detection/Machine vision/Optical model/High-quality images/Contrast of defects分类
信息技术与安全科学引用本文复制引用
吴禄慎,张丛,万超,史皓良..基于机器视觉的钢轨表面检测光学模型的研究[J].铁道标准设计,2017,61(5):50-53,57,5.基金项目
南昌大学研究生创新专项基金(cx2015059) （cx2015059）
