Surface Defect Detection of Cabbage Based on Curvature Features of 3D Point Cloud

文献类型： 外文期刊

第一作者： Gu, Jin

作者： Gu, Jin;Zhang, Yawei;Deng, Junwen;Zhang, Bin;Yin, Yanxin;Yin, Yanxin;Wang, Ruixue

作者机构：

关键词： defect detection; cabbage; curvature features; 3D point cloud; depth camera

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： The dents and cracks of cabbage caused by mechanical damage during transportation have a direct impact on both commercial value and storage time. In this study, a method for surface defect detection of cabbage is proposed based on the curvature feature of the 3D point cloud. First, the red-green-blue (RGB) images and depth images are collected using a RealSense-D455 depth camera for 3D point cloud reconstruction. Then, the region of interest (ROI) is extracted by statistical filtering and Euclidean clustering segmentation algorithm, and the 3D point cloud of cabbage is segmented from background noise. Then, the curvature features of the 3D point cloud are calculated using the estimated normal vector based on the least square plane fitting method. Finally, the curvature threshold is determined according to the curvature characteristic parameters, and the surface defect type and area can be detected. The flat-headed cabbage and round-headed cabbage are selected to test the surface damage of dents and cracks. The test results show that the average detection accuracy of this proposed method is 96.25%, in which, the average detection accuracy of dents is 93.3% and the average detection accuracy of cracks is 96.67%, suggesting high detection accuracy and good adaptability for various cabbages. This study provides important technical support for automatic and non-destructive detection of cabbage surface defects.

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[1]Surface Defect Detection of Cabbage Based on Curvature Features of 3D Point Cloud. Gu, Jin,Zhang, Yawei,Deng, Junwen,Zhang, Bin,Yin, Yanxin,Yin, Yanxin,Wang, Ruixue. 2022

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