Area of research
Plant Science · Computer Vision and Pattern Recognition
Research interest
Research interests include Artificial intelligence, Computer science, Computer vision, Rigid body, Pattern recognition (psychology), and Robustness (evolution).
Deep Learning-Based Seedling Row Detection and Localization Using High-Resolution UAV Imagery for Rice Transplanter Operation Quality Evaluation
Dynamic mutual training semi-supervised semantic segmentation algorithm with adaptive capability (AD-DMT) for choy sum stem segmentation and 3D positioning of cutting points
A visual measurement method for slider dimensions combining sub-pixel counting and line segment cluster processing strategy
A Novel Interpolation Method for Soil Parameters Combining RBF Neural Network and IDW in the Pearl River Delta
Improved Multi-Size, Multi-Target and 3D Position Detection Network for Flowering Chinese Cabbage Based on YOLOv8
A Lightweight Method for Ripeness Detection and Counting of Chinese Flowering Cabbage in the Natural Environment
Design and Testing of a 2-DOF Adaptive Profiling Header for Forage Harvesters
Damage Detection of Unwashed Eggs through Video and Deep Learning
Detection of the foreign object positions in agricultural soils using Mask-RCNN
An improved YOLOv5 model: Application to leaky eggs detection
A Method of Measuring the Absolute Position and Attitude Parameters of a Moving Rigid Body Using a Monocular Camera
A Method for Measuring the Absolute Position and Attitude Parameters of a Moving Rigid Body Using a Monocular Camera
Research on Real-time Identification and Tracking Method of Defective Eggs Based on Deep Learning
A Smartphone-Based Six-DOF Measurement Method With Marker Detector
Multi-Camera-Based Universal Measurement Method for 6-DOF of Rigid Bodies in World Coordinate System
Rigid Body 6-DOF Measurement Method Realized by Total Station without Leveling
A Research On Rice Transplanter Fitted with Seedling Number Counting Device