基于机器视觉的螺纹钢表面缺陷检测方法

螺纹钢是一种广泛应用的建筑材料，在轧制过程中如果不能及时发现其尺寸和表面缺陷，就会生产出大量废品，给企业带来损失.本文设计了一种基于视觉的螺纹钢表面缺陷检测方法.先利用仿射变换对图像中歪斜的螺纹钢进行校正，然后基于霍夫变换检测纵肋边缘直线位置的方法对螺纹钢正面、侧面图像进行区分.最后针对正面、侧面图像分别进行缺陷检测，快速准确地判别表面是否存在缺陷.实验表明所设计的方法具有较好的稳定性和实用性，能有效地解决人工检测过程中效率低、误检率高等问题.

Rebar Surface Defect Detection Method Based on Machine Vision

Rebar is a widely used building material, if its size and surface defects cannot be found in time in the rolling process, it will produce a large number of waste products and bring losses to the enterprise. In this study, we design a rebar surface defect detection method based on machine vision. Firstly, the skew rebar in the image is corrected by affine transformation, and then the front and side images of rebar are distinguished based on Hough transform to detect the straight line position of longitudinal rib edge. Finally, defect detection is carried out for front and side images to quickly and accurately judge whether there are defects on the surface. Experiments show that the design method has sound stability and practicality. It can effectively solve the problems of low efficiency and high false detection rate in the process of manual detection.