摘要
为实现微旋钮的智能调控,获取旋钮准确位姿为关键任务。基于此,提出了一种融合边缘检测与深度网络的旋钮位姿测量方法。首先,针对图像倾斜产生测量误差的问题,提出改进的Canny算法提取准确边缘,并结合透视变换对其矫正;其次,利用改进的YOLO-V4算法实现旋钮的精确分割;最后应用改进的Canny算法与双三次样条插值提取高精度旋钮凹槽亚像素轮廓,通过PCA算法拟合轮廓矩形并测量位姿。实验结果表明,所提改进Canny算法的边缘提取精度提高,有效减少了虚假边缘;改进的YOLO-V4旋钮检测算法相较于YOLO-V4平均检测精度提升了2.92%,达到99.49%;旋钮凹槽中心位置与偏转角度的测量精度分别达到1.5pixel和1.5°,可实现旋钮位姿的高精度测量。
In order to realize the intelligent control of the micro knob, the key task is to obtain the accurate position and pose of the knob. Based on this, a new method of knob pose measurement combining edge detection and deep network is proposed. Firstly, to solve the problem of measurement error caused by image tilt, an improved Canny algorithm was proposed to extract the accurate edge and correct it by combining perspective transform. Secondly, the improved YOLO-V4 algorithm is used to achieve the precise segmentation of the knob. Finally, the improved Canny algorithm and bicubic spline interpolation were used to extract the high-precision knob groove sub-pixel contour, and the PCA algorithm was used to fit the contour rectangle and measure the position and pose. Experimental results show that the proposed improved Canny algorithm improves the accuracy of edge extraction and effectively reduces false edges. Compared with the YOLO-V4 knob detection algorithm, the average detection accuracy of the improved YOLO-V4 knob detection algorithm is improved by 2.92%, reaching 99.49%. The measurement accuracy of the center position and deflection angle of the knob grooves reaches 1.5 pixel and 1.5° respectively, which can realize the high-precision measurement of the knob position and pose.
作者
朱志玲
周志峰
赵勇
王永泉
王立端
Zhu Zhiling;Zhou Zhifeng;Zhao Yong;Wang Yongquan;Wang Liduan(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Shanxi New Energy Technology Co.,Ltd.,Taiyuan 030024,China;Shanghai Compass Satellite Navigation Technology Co.,Ltd.,Shanghai 201801,China)
出处
《电子测量技术》
北大核心
2021年第17期26-32,共7页
Electronic Measurement Technology
基金
上海市科学技术委员会科研基金(17511106700)项目资助。
