基于视觉处理的钢材尺寸缺失检测方法

Steel Size Missing Detection Method Based on Visual Processing

在钢材质量检测过程中,需要准确测量钢材的横切面尺寸,计算钢材缺失。针对高精密钢材,传统的机械\人工等缺失检测方法,无法避免由于放置位置、操作流程等原因造成的检测误差,从而降低了检测的准确性。因此,提出了一种基于智能计算机视觉处理的钢材横切面尺寸缺失检测方法。采集钢材横切面图像,对该图像进行二值化处理。提取钢材图像中的边缘特征点,引用一种最大熵方法,设定合理的阀值,进行钢材横切面尺寸缺失检测。实验结果表明,利用这种算法进行钢材横切面尺寸缺失检测,极大地提高了检测的准确性,取得了令人满意的结果。

In automobile steel quality inspection process, need accurate measuring automobile steel section size. Use the traditional test machine for cross section size missing detection, cannot avoid the place position, operating process caused by the detection error, so as to reduce the detection accuracy. Therefore, a new method based on image processing car steel section size missing detection method. Extraction of image edge feature point, the use of the maximum entropy method for automobile steel section size missing detection. The experimental results show that using this algorithm auto-mobile steel section size missing detection, greatly improve the detection accuracy, and achieved satisfactory results.