Gu Jin, Zhang Yawei, Yin Yanxin, Wang Ruixue, Deng Junwen, Zhang Bin
College of Engineering, China Agricultural University, Beijing, China.
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Front Plant Sci. 2022 Jul 14;13:942040. doi: 10.3389/fpls.2022.942040. eCollection 2022.
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.
卷心菜在运输过程中因机械损伤产生的凹痕和裂缝，对其商业价值和储存时间都有直接影响。本研究提出一种基于三维点云曲率特征的卷心菜表面缺陷检测方法。首先，使用RealSense-D455深度相机采集红-绿-蓝（RGB）图像和深度图像，用于三维点云重建。然后，通过统计滤波和欧几里得聚类分割算法提取感兴趣区域（ROI），从背景噪声中分割出卷心菜的三维点云。接着，基于最小二乘平面拟合方法，利用估计的法向量计算三维点云的曲率特征。最后，根据曲率特征参数确定曲率阈值，从而检测出表面缺陷类型和面积。选取平头卷心菜和圆头卷心菜对凹痕和裂缝等表面损伤进行测试。测试结果表明，该方法的平均检测准确率为96.25%，其中凹痕的平均检测准确率为93.3%，裂缝的平均检测准确率为96.67%，表明该方法检测准确率高，对各种卷心菜具有良好的适应性。本研究为卷心菜表面缺陷的自动无损检测提供了重要的技术支持。
