文章摘要
刘昶,张鑫,朱立瑶.基于SVM的捆装圆棒材计数方法[J].包装工程,2020,41(3):224-229.
LIU Chang,ZHANG Xin,ZHU Li-yao.Bundled Round Bars Counting Method Based on SVM[J].Packaging Engineering,2020,41(3):224-229.
基于SVM的捆装圆棒材计数方法
Bundled Round Bars Counting Method Based on SVM
投稿时间:2019-10-08  修订日期:2020-02-10
DOI:10.19554/j.cnki.1001-3563.2020.03.035
中文关键词: 计算机视觉  机器学习  方向梯度直方图  支持向量机
英文关键词: computer vision  machine learning  HOG  SVM
基金项目:辽宁省自然科学基金(20170540792)
作者单位
刘昶 沈阳理工大学沈阳 110159 
张鑫 沈阳理工大学沈阳 110159 
朱立瑶 沈阳理工大学沈阳 110159 
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中文摘要:
      目的 研究利用相机拍摄的捆装棒材端面图像进行棒材的自动计数方法。方法 利用Hough变换提取端面圆的半径对图像尺寸进行规格化处理,使每个圆形端面具有近似统一的尺寸;利用滑动窗口的方法对检测窗口的中心点是否为圆棒材的中心进行判别,并标记判别结果;判别过程采用了一种SVM分类器的方法;通过在标记图上进行连通区搜索,统计棒材中心点区域的个数实现棒材计数;在测试图像库上对方法的有效性进行了测试实验。结果 实验结果显示,文中方法在测试库上的正确率达到94%,其性能较模板匹配方法有显著优势。结论 该方法的正确率较常规方法有很大提升,并且具有较好的鲁棒性,可应用于捆装圆棒材的自动计数问题。
英文摘要:
      The work aims to study an automatic counting method of bundled round bars from the image of their bottom surface caught by a camera. The radius of each bar's bottom surface was extracted by the Hough transformation method for normalizing. Then the image was zoomed to make every circle have a standard radius. The sliding window method was used to determine whether the center of the detection window was the center of a round bar. The result was labeled on another image. SVM classifiers were used for detection. The counting result was obtained by counting the number of connected regions in the labeled image of marked centers. Finally, the effectiveness of the method was tested with images in the test library. The experiment results showed that the accuracy of the methods in this paper on the test library was 94%, which was much higher than ordinary template matching method. The accuracy of the method in this paper is improved greatly compared with ordinary method. It also has a good robustness, and can be applied to automatic counting system for bundled round bars.
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