AUTHOR=Gu Jin , Zhang Yawei , Yin Yanxin , Wang Ruixue , Deng Junwen , Zhang Bin 

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

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.942040

DOI=10.3389/fpls.2022.942040

ISSN=1664-462X

ABSTRACT=The dents and cracks of cabbage caused by mechanical damage during the transportation has a direct impact both on the commercial value and storage time. In this paper, a method for surface defects detection of cabbage is proposed based on curvature feature of 3D point cloud. Firstly, the RGB images and depth images are collected by RealSense-D455 depth camera for 3D point cloud reconstruction. Then, the region of interest is extracted by statistical filtering and Euclidean clustering segmentation algorithm and the 3D point cloud of cabbage is segmented from background noise. And then, the curvature features of the 3D point cloud are calculated by 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 defects 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 an important technical support for automatic and nondestructive detection of cabbage surface defects.