Chongxin Hu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Chongxin Hu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Junwei Zhu
College of Mechatronics and Control Engineering, Shenzhen University, Nanhai Ave., Shenzhen 518060, China
1 shared publication - Weixiang Liu
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
1 shared publication - Xiaopin Zhong
Shenzhen University
1 shared publication - Yuanlong Deng
School of Sino-Germany Intelligent Production, Shenzhen City Polytechnic (Shenzhen Institute of Technology), Shenzhen 518116, China
1 shared publication - Zongze Wu
Shenzhen University
1 shared publication
Latest Publications
- Open Access
An Overview of Image Generation of Industrial Surface Defects
Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The performance of these methods in generating defect images is analysed through various indices. This paper discusses the existing methods, highlights the shortcomings and challenges in the field of defect image generation, and proposes future research directions. Finally, the paper concludes with a summary.
Open AccessAn Overview of Image Generation of Industrial Surface Defects
Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The performance of these methods in generating defect images is analysed through various indices. This paper discusses the existing methods, highlights the shortcomings and challenges in the field of defect image generation, and proposes future research directions. Finally, the paper concludes with a summary.