I am pursuing Ph.D. degree in School of Mechanical Science and Engineering, Huazhong University of Science and Technology. Previously, I received my M.S. degree in School of Mechanical Science and Engineering, Huazhong University of Science and Technology (2021), and B.S. degree in Hubei University of Technology (2019).
Now, I am a visiting student in A*STAR Centre for Frontier AI Research (CFAR), Singapore, supervised by Joey Tianyi Zhou and Jiawei Du.
My current research interest is computer vision including object detection, image segmentation, and multimodal fusion for autonomous driving.
📖 Educations
- 2023.05 - Now, Visiting Ph.D. Student, A*STAR Centre for Frontier AI Research (CFAR).
- 2021.09 - Now, Ph.D. Student, School of Mechanical Science and Engineering, Huazhong University of Science and Technology.
- 2019.06 - 2021.06, M.Eng., School of Mechanical Science and Engineering, Huazhong University of Science and Technology.
- 2015.09 - 2019.06, B.Eng., School of Mechanical Engineering, Hubei University of Technology.
💻 Internships
- 2022.07 - 2022.09, Huawei Intelligent Automotive Solution Business Unit, Shanghai, China.
👨🎓 Academic Service
- Reviewer
IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Industrial Informatics
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Artificial Intelligence
IEEE Sensors Journal
IEEE Access
IET Image Processing
Neural Processing Letters
Signal, Image and Video Processing
- Conference Service
Program Committees of 2023 the 1st International Conference on AI-generated Content (AIGC2023)
📝 Publications
Moyun Liu, Bing Chen, Youping Chen, Jingming Xie, Lei Yao, Yang Zhang, Qin Hu, Jiawei Du, Joey Tianyi Zhou
IEEE Transactions on Intelligent Vehicles, 2024
- We analyze the contribution of camera image for depth completion and propose a better unguided depth completion framework.
MENet: Multi-Modal Mapping Enhancement Network for 3D Object Detection in Autonomous Driving
Moyun Liu, Youping Chen, Jingming Xie, Yijie Zhu, Yang Zhang, Lei Yao, Zhenshan Bing, Genghang Zhuang, Kai Huang, Joey Tianyi Zhou
IEEE Transactions on Intelligent Transportation Systems, 2024
- We propose a multi-modal mapping enhancement network named MENet for 3D object detection.
A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
Moyun Liu, Bing Chen, Youping Chen, Jingming Xie, Lei Yao, Yang Zhang, Joey Tianyi Zhou
Knowledge-Based Systems, 2024 |
- We propose a fast and effective depth completion network based on the image guidance.
LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image
Moyun Liu, Youping Chen, Jingming Xie, Lei He, Yang Zhang
IEEE Sensors Journal, 2023 |
- We propose a weld defect detection method based on convolution neural network, namely Lighter and Faster YOLO (LF-YOLO).
Yang Zhang, Moyun Liu, Huiming Zhang, Guodong Sun, and Jingwu He
Pattern Recognition, 2023
- We propose an adaptive fusion affinity graph (AFA-graph) with noise-free low-rank representation in an online manner for natural image segmentation
A lightweight and accurate recognition framework for signs of X-ray weld images
Moyun Liu, Jingming Xie, Jing Hao, Yang Zhang, Xuzhan Chen, Youping Chen
Computers in Industry, 2022
- We propose a signs recognition framework based on convolutional neural networks (CNNs) for weld images. The proposed framework firstly contains a shallow classification network for correcting the pose of images, and a narrow network for final weld information recognition.
A Unified Light Framework for Real-time Fault Detection of Freight Train Images
Yang Zhang, Moyun Liu*, Yang Yang, Yanwen Guo, Huiming Zhang
IEEE Transactions on Industrial Informatics, 2021
- We propose a unified light framework to improve detection accuracy while supporting a real-time operation with a low resource requirement.
Affinity fusion graph-based framework for natural image segmentation
Yang Zhang, Moyun Liu, Jingwu He, Fei Pan, Yanwen Guo
IEEE Transactions on Multimedia, 2021 |
- We propose an affinity fusion graph framework to effectively connect different graphs with highly discriminating power and nonlinearity for natural image segmentation.