基于线结构光的柑橘分离线视觉检测方法研究。

IF 2.7 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Qingcang Yu, Song Xue, Yang Zheng
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引用次数: 0

摘要

柑橘分离系的检测是柑橘加工行业的关键环节。受线结构光技术在表面缺陷检测中的成果启发,本文提出了一种基于线结构光的柑橘分离线检测方法。首先,采用伽玛校正的Otsu方法提取图像中的激光条纹区域;其次,采用改进的骨架提取算法,在获取柑橘表面三维点云数据的同时,消除了原有骨架提取算法固有的分岔误差;最后,采用最小二乘渐进迭代逼近算法逼近理想曲面曲线;随后,使用主成分分析来推导这条理想拟合曲线的法线。然后将每个点(沿其对应法线方向)与实际几何特征曲线的偏差作为分离线定位的定量指标。提取的分离线与人工定义的标准分离线的平均相似度达到92.5%。总体而言,该方法获得的分离线上95%的点误差小于4像素。实验结果表明,通过几何特征的定量偏差分析,实现了分割线的自动检测与定位,满足了柑橘自动分割线高精度、无损化的要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Vision Method for Detecting Citrus Separation Lines Using Line-Structured Light.

The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a gamma-corrected Otsu method is employed to extract the laser stripe region from the image. Secondly, an improved skeleton extraction algorithm is employed to mitigate the bifurcation errors inherent in original skeleton extraction algorithms while simultaneously acquiring 3D point cloud data of the citrus surface. Finally, the least squares progressive iterative approximation algorithm is applied to approximate the ideal surface curve; subsequently, principal component analysis is used to derive the normals of this ideally fitted curve. The deviation between each point (along its corresponding normal direction) and the actual geometric characteristic curve is then adopted as a quantitative index for separation lines positioning. The average similarity between the extracted separation lines and the manually defined standard separation lines reaches 92.5%. In total, 95% of the points on the separation lines obtained by this method have an error of less than 4 pixels. Experimental results demonstrate that through quantitative deviation analysis of geometric features, automatic detection and positioning of the separation lines are achieved, satisfying the requirements of high precision and non-destructiveness for automatic citrus splitting.

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来源期刊
Journal of Imaging
Journal of Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.90
自引率
6.20%
发文量
303
审稿时长
7 weeks
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