基于图像处理的光学晶体维氏硬度测量算法

Vickers hardness measurement algorithm for optical crystals based on image processing

硬度是衡量材料软硬程度的一种性能指标。针对镥铝石榴石(lutetium-aluminum garnet,LuAG)和镥镓石榴石(lutetium-gallium garnet,LuGG)等光学晶体弹性小、受压易破损、压痕边界不清晰的特点,提出一种基于图像处理的光学晶体维氏硬度测量方法。利用深度学习YOLOv5s网络,从图像中分割出维氏压痕目标位置,进行自适应二值化,选取最大连通域、骨架提取,进一步通过概率霍夫直线检测特定方向线段,得到压痕4个顶点的准确位置。实验结果表明,平均相对误差可以控制在1.5%以内,有效降低了传统网络算法和传统图像处理算法的计算误差,适用于对光学晶体维氏硬度的自动化精确测量。

Hardness is a performance index to measure the degree of softness and hardness of materials.In view of the characteristics of lutetium-aluminum garnet(LuAG),lutetium-gallium garnet(LuGG)and other optical crystals with small elastic compression,easy to break and unclear indentation boundary.An optical crystal Vickers hardness measurement method based on image processing was proposed.A deep learning YOLOv5s network was used to segment the image.The exact positions of the four vertices were obtained by adaptive binarization,maximum connected domain selection,skeleton extraction and then specific directional line segments detected by probabilistic Hough line treatment.The experimental results show that the average relative error can be controlled within 1.5%,which effectively reduces the calculation error of traditional network algorithm and traditional image processing algorithm,and is suitable for automatic and accurate measurement of optical crystal Vickers hardness.