Haikuan Wang
Papers
2
Total Citations
6
H-Index
2
About
Haikuan Wang is a researcher specializing in computer vision and deep learning, with a particular focus on object detection algorithms for robotics and industrial inspection. His work centers on enhancing the accuracy and efficiency of YOLO-based models, a family of real-time object detection frameworks. Wang’s major contributions include the development of an improved YOLOv5 algorithm tailored for basketball robots, enabling precise real-time detection of balls and players in dynamic sports environments. He further advanced the field with YOLOv8_CB, an enhanced model integrating Convolutional Block Attention Modules (CBAM) and Bidirectional Feature Pyramid Networks (BiFPN) to detect defects in pipeline girth welds, addressing critical needs in industrial quality control. While his most-cited papers have garnered modest attention—4 and 2 citations respectively—they reflect emerging work in niche applications where accuracy and speed are paramount. Wang’s research demonstrates a practical bridge between state-of-the-art object detection and real-world challenges in robotics and manufacturing, positioning him as a contributor to applied AI solutions.
Research Focus
Key Achievements
Top Papers
- 1Object Detection Algorithm Based on Improved YOLOv5 for Basketball Robot4 citations · 2022
- 2