Liang Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Liang Zhang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Chenghao Liao
School of Engineering, Huzhou University, Huzhou 313000, China
1 shared publication - Haibo Huang
School of Mechanical and Electrical Engineering Jiangsu Provincial Key Laboratory of Embodied Intelligence Robotics Soochow University Suzhou China
1 shared publication - Qingzhu Zhang
School of Engineering, Huzhou University, Huzhou 313000, China
1 shared publication - Siqi Li
School of Engineering, Huzhou University, Huzhou 313000, China
1 shared publication - Yang Shen
School of Engineering, Huzhou University, Huzhou 313000, China
1 shared publication - Ying Zang
School of Information Engineering, Huzhou University, Huzhou, China
1 shared publication
Latest Publications
- Open Access
Enhanced Hand–Eye Coordination Control for Six-Axis Robots Using YOLOv5 with Attention Module
Utilizing machine vision technology based on YOLOv5, a six-axis robot can quickly identify and classify targets. However, when the YOLOv5 model is used for the recognition and grasping of small workpieces, issues such as low precision and missed detections frequently occur. This paper proposes an enhanced object recognition algorithm, integrating a CBAM attention module and an improved loss function into YOLOv5 to control the hand–eye coordination of the six-axis robot during grasping. The CBAM attention module is incorporated into the backbone network of YOLOv5 to enhance its feature extraction capabilities, while the original loss function is modified to accelerate convergence and improve regression accuracy. An experimental platform for six-axis robot hand–eye coordination grasping was built, and grasping experiments were conducted. The proposed method significantly improves the robot’s grasping accuracy, with a 99.59% mAP0.5 and a 90.83% successful grasping rate, effectively addressing the challenges of low accuracy and missed detections in traditional systems.
Open AccessEnhanced Hand–Eye Coordination Control for Six-Axis Robots Using YOLOv5 with Attention Module
Utilizing machine vision technology based on YOLOv5, a six-axis robot can quickly identify and classify targets. However, when the YOLOv5 model is used for the recognition and grasping of small workpieces, issues such as low precision and missed detections frequently occur. This paper proposes an enhanced object recognition algorithm, integrating a CBAM attention module and an improved loss function into YOLOv5 to control the hand–eye coordination of the six-axis robot during grasping. The CBAM attention module is incorporated into the backbone network of YOLOv5 to enhance its feature extraction capabilities, while the original loss function is modified to accelerate convergence and improve regression accuracy. An experimental platform for six-axis robot hand–eye coordination grasping was built, and grasping experiments were conducted. The proposed method significantly improves the robot’s grasping accuracy, with a 99.59% mAP0.5 and a 90.83% successful grasping rate, effectively addressing the challenges of low accuracy and missed detections in traditional systems.