Hong Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Hong Zhang has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Fei Wu
College of Mechanical and Vehicle Engineering, Chongqing University 1 , Chongqing 400044
1 shared publication - Fuhao Wang
State Key Laboratory of Mechanical Transmission for Advanced Equipment, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China
1 shared publication - Gang Wang
State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, China
1 shared publication - Haijun Zhang
State Key Laboratory of Mechanical Transmission for Advanced Equipment, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China
1 shared publication - Huayan Pu
Chongqing University
1 shared publication - Jun Luo
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China
1 shared publication
Latest Publications
- 21 March 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 73 (00189456) , 1-12
A Framework for Synchronous Extraction of Multitype Steel Mesh Features Based on Measured Point Clouds
In large infrastructure projects, the precision and quality of welding large-scale steel mesh sheets are crucial for the safety of the building structure. Traditional manual welding is inefficient and risky, thus, there is an urgent need to replace manual labor with specialized robots equipped with visual recognition systems. However, identifying features like weld points on diverse steel components remains a bottleneck for the application of robotic welding technology. This paper proposes a framework for simultaneous extraction of multi-type steel mesh features based on measured point clouds to meet the requirements of a visual-guided robotic welding system. Initially, Euclidean clustering method is employed to segment the point cloud and obtain all point cloud clusters that belong to the rebar body. Subsequently, Principal Component Analysis (PCA) and geometric constraints are utilized to merge the point cloud clusters belonging to the same rebar or overlapping rebar. Finally, feature extraction of rebar’s intersection welding points and lap weld seams is performed using methods such as Statistical Outlier Removal (SOR) and Least Squares Cylinder Fitting. Additional ablation experiments were conducted to assess the robustness of the framework. To substantiate the feasibility of the framework, the recommended approach was compared with existing point cloud-based and image-based methods. The results indicate that the framework achieves a balance between efficiency and stable robustness while ensuring recognition accuracy.
- 21 March 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 73 (00189456) , 1-12
A Framework for Synchronous Extraction of Multitype Steel Mesh Features Based on Measured Point Clouds
In large infrastructure projects, the precision and quality of welding large-scale steel mesh sheets are crucial for the safety of the building structure. Traditional manual welding is inefficient and risky, thus, there is an urgent need to replace manual labor with specialized robots equipped with visual recognition systems. However, identifying features like weld points on diverse steel components remains a bottleneck for the application of robotic welding technology. This paper proposes a framework for simultaneous extraction of multi-type steel mesh features based on measured point clouds to meet the requirements of a visual-guided robotic welding system. Initially, Euclidean clustering method is employed to segment the point cloud and obtain all point cloud clusters that belong to the rebar body. Subsequently, Principal Component Analysis (PCA) and geometric constraints are utilized to merge the point cloud clusters belonging to the same rebar or overlapping rebar. Finally, feature extraction of rebar’s intersection welding points and lap weld seams is performed using methods such as Statistical Outlier Removal (SOR) and Least Squares Cylinder Fitting. Additional ablation experiments were conducted to assess the robustness of the framework. To substantiate the feasibility of the framework, the recommended approach was compared with existing point cloud-based and image-based methods. The results indicate that the framework achieves a balance between efficiency and stable robustness while ensuring recognition accuracy.