| 毛向向,王红军.薄壁零件复杂光照情况下的轮廓特征识别[J].电子测量与仪器学报,2021,35(3):137-143 |
| 薄壁零件复杂光照情况下的轮廓特征识别 |
| Improved retinex and edge detection fusion of thin walled complex part contour recognition algorithm |
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| DOI： |
| 中文关键词: 轮廓识别 Retinex 薄壁零件 Canny算法 |
| 英文关键词:contour recognition Retinex thin walled parts Canny algorithm |
| 基金项目:促进高校内涵发展 应急攻关项目（5212010976）、国家自然科学基金（51575055）项目资助 |
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| Author | Institution |
| Mao Xiangxiang | 1.School of Mechanical Electrical Engineering, Beijing Information Science ＆ Technology University, Beijing 100192, China; |
| Wang Hongjun | 1.School of Mechanical Electrical Engineering,Beijing Information Science ＆ Technology University, Beijing 100192, China;2.Beijing International Science Cooperation Base of High End Equipment Intelligent Perception and Control, Beijing 100192, China;
3.Key Laboratory of Modern Measurement ＆ Control Technology, Ministry of Education, Beijing Information
Science ＆ Technology University, Beijing 100192, China |
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| 摘要点击次数: 1119 |
| 全文下载次数: 945 |
| 中文摘要: |
| 针对工业生产线薄壁零件识别中存在的零件轮廓识别受光照影响较大的问题，将颜色恒常性技术应用到工业生产线轮廓识别中，基于Retinex、HSI及边缘检测算法的基本原理，提出了一种薄壁零件复杂光照情况下的轮廓特征识别算法用于薄壁零件的图像恢复和轮廓识别。该方法首先使用HSI颜色空间对图像的亮度进行提取，然后利用改进Retinex算法来对获取的零件图象进行自适应图像增强，滤除图像中的光照变化信息，之后在此基础上对图像进行灰度化处理，最后采用Canny算法识别薄壁零件的边缘，进一步提取薄壁零件的有效轮廓特征。实验结果表明:该算法能够快速、准确地识别复杂光照情况下的薄壁零件的轮廓信息，满足工业流水线检测的需求。 |
| 英文摘要: |
| Aiming at the problem that part contour recognition in the recognition of thin walled parts in industrial production lines is greatly affected by light, the color constancy technology is applied to the contour recognition of industrial production lines. Based on the basic principles of Retinex, HSI and edge detection algorithms, a contour feature recognition algorithm for thin walled parts under complex lighting conditions is proposed for image restoration and contour recognition of thin walled parts. First, the method uses HSI color space to extract the brightness of the image. Then the improved Retinex algorithm is used to perform adaptive image enhancement on the acquired part image and the light change information in the image is filtered out. Then the image is grayed out on this basis. Finally, the Canny algorithm is used to identify the edges of thin walled parts, and the valid outline features of thin walled parts are further extracted. Experimental results show that the algorithm can quickly and accurately identify the outline information of thin walled parts under complex lighting conditions, and meet the needs of industrial assembly line inspection. |
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