Papers
3
Total Citations
14
H-Index
2
About
Zhihao Lai is a researcher at the forefront of applying deep learning to agricultural and industrial automation, with a primary focus on computer vision and intelligent robotics. His work centers on developing advanced object detection and segmentation algorithms—particularly YOLO-based architectures—to solve real-world challenges in unstructured environments. Lai’s major contributions include pioneering the YOLO-OBB approach for precise citrus fruit stem pose estimation, enabling robots to identify optimal picking points even when stems are obscured by foliage or overlapping fruit. He has also advanced visual navigation for tea plantations by enhancing the DeeplabV3+ model with novel feature extraction modules, improving path recognition accuracy in complex terrains. Additionally, his early work on YOLOv5-based state detection for switchgear panels has been cited 8 times, demonstrating its relevance to power system safety. With over 14 total citations across his most-cited papers, Lai’s research bridges the gap between cutting-edge AI and practical automation, offering scalable solutions for precision agriculture and industrial inspection. His innovative integration of attention mechanisms and regression methods marks him as a rising contributor to intelligent systems engineering.
Research Focus
Key Achievements
Top Papers
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