电子测量与仪器学报

童胜杰,江明,焦传佳.一种改进工件边缘检测方法的研究[J].电子测量与仪器学报,2021,35(1):128-134

一种改进工件边缘检测方法的研究

Research on an improved edge detection method of workpiece

DOI：

英文关键词:edge detection second order differential operator mathematical morphology image filtering

基金项目:国家自然科学基金（61271377）资助项目

作者	单位
童胜杰	安徽工程大学高端装备先进感知与智能控制教育部重点实验室芜湖241000；安徽工程大学电气工程学院芜湖241000
江明	安徽工程大学高端装备先进感知与智能控制教育部重点实验室芜湖241000；安徽工程大学电气工程学院芜湖241000
焦传佳	安徽工程大学高端装备先进感知与智能控制教育部重点实验室芜湖241000；安徽工程大学电气工程学院芜湖241000

Author	Institution
Tong Shengjie	Key Laboratory of Advanced Perception and Intelligent Control of Highend Equipment, Anhui Polytechnic University,Wuhu 241000, China；School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Jiang Ming	Key Laboratory of Advanced Perception and Intelligent Control of Highend Equipment, Anhui Polytechnic University,Wuhu 241000, China；School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Jiao Chuanjia	Key Laboratory of Advanced Perception and Intelligent Control of Highend Equipment, Anhui Polytechnic University,Wuhu 241000, China；School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China

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中文摘要:

针对现有边缘检测技术难以同时消除图像中噪声和工件表面划痕对边缘检测的影响，并保持图像边缘的清晰度和连续性，提出了一种基于二阶微分算子和数学形态学的改进边缘检测技术。首先利用数学形态学理论，设计了一种形态学开闭运算处理图像的方法，为去除工件表面划痕做好预处理；然后用二阶微分Laplace算子对预处理后的图像进行边缘检测；最后改进了一种高斯与双边滤波结合的算法，强化去噪效果，并对最终算法进行实验验证。实验结果表面，改进的算法在去除工件表面划痕方面效果明显，并与传统微分算子比较，边缘清晰度、峰值信噪比（PSNR）都有大幅提高，为提高工件识别精度打好基础。

英文摘要:

It is difficult for the existing edge detection technology to eliminate the influence of noise and scratch on the edge detection, and maintain the clarity and continuity of image edge. An improved edge detection technique based on second order differential operator and mathematical morphology is proposed. Firstly, considering the morphological characteristics, the image processing method of open and closed morphological operation is improved to remove the scratches on the workpiece surface in advance. Then the edges of the workpiece are detected by the second order differential Laplace operator. Finally, in order to achieve better image denoising effect, a kind of enhanced denoising combining Gaussian and bilateral filtering is improved, and the final algorithm is verified by experiments. Experimental results show that the improved algorithm is effective in removing the surface scratches of the workpiece, and the edge clarity and peak signal to noise ratio (PSNR) are greatly improved, laying a foundation for improving the workpiece identification accuracy.

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