Congratulations to Li Ruiqi on graduating successfully!

Li Ruiqi graduated with a Bachelor’s degree from Shanghai University and began pursuing an academic master’s degree at the School of Computer Engineering and Science, Shanghai University, in 2020. Starting from the final year of their undergraduate studies, Li Ruiqi joined the research group led by Professor Han Yuexing to study material image processing techniques and applications. Under the careful guidance of Professor Han, Li Ruiqi continued and advanced the following research studies:

1. Automated Detection of Chirikov Patterns. The structure and orientation information of crystals can be obtained by analyzing the Electron Backscatter Diffraction (EBSD) patterns, which are acquired through EBSD devices. The reliability and accuracy of the obtained information depend on the precise localization of EBSD pattern bands and intersection points. In this research, a method is proposed that combines Radon transform and cumulative probability Hough transform to achieve automatic localization of EBSD patterns (Chirikov bands) and intersection points. Experimental results demonstrate that this method is robust and capable of detecting more accurate Chirikov bands and intersection points.

2. To address the challenge of balancing annotation cost and segmentation accuracy in material image segmentation tasks and achieve real-time segmentation models, a material image segmentation algorithm based on interactive drawing annotation and machine learning is designed. The primary objective of this method is to obtain segmentation models in real-time. It involves extracting neighborhood features around the center points of material images and performing multiple rounds of interactive drawing annotation. An incremental learning approach is utilized to train the final image segmentation model. This approach enables the model to continuously improve and adapt to new data while minimizing the annotation effort required.

3. To achieve high-precision segmentation models based on limited and easily accessible annotated data, a weakly supervised deep learning method for material image segmentation is designed, which relies on pseudo-labeling. The objective of this method is to obtain optimal segmentation results based on limited annotated data. A novel dual-branch network architecture is designed, and a new context feature discrepancy supervision loss is proposed to generate pseudo-labels. This allows the model to achieve high segmentation accuracy using only training images annotated with scribbles. This method addresses the challenges of small sample sizes, annotation difficulties, and resource wastage during testing in deep learning neural networks for material image segmentation tasks.

4. Due to the rapid nature of phase transformations and the complex morphology of lamellar martensite, traditional studies based on martensite images struggle to provide sufficient information during the phase transformation process. In this research, a novel video processing method is proposed to extract and analyze information data from videos depicting the transformation of lamellar martensite. The analysis of image data provides the following dynamic information about the transformation of lamellar martensite: the number of transformations, maximum length, maximum width, average length, average width, area, and category orientation. This study breaks the limitations of studying martensite based on static images and comprehensively describes various data and information related to the dynamic transformation of martensite.

After graduating, Li Ruiqi joined Lianying Intelligent to engage in software development-related work. Throughout Li Ruiqi’s three-year graduate studies at Shanghai University, they diligently pursued learning, continuously enhancing their professional knowledge and research presentation skills. Li Ruiqi had the privilege of meeting many excellent mentors and friends. We hope that Li Ruiqi will always remember their original aspirations and mission, overcome challenges, and forge ahead on their future path with determination.