Jingwen Cui
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jingwen Cui has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Bowen Zheng
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
1 shared publication - Guiling Sun
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
1 shared publication - Jianping Zhang
Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
1 shared publication
Latest Publications
- Open Access
Extraction and Research of Crop Feature Points Based on Computer Vision
Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.
Open AccessExtraction and Research of Crop Feature Points Based on Computer Vision
Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.