Dongxin Fan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Dongxin Fan has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Bo Liang
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
1 shared publication - Jianping Xiong
Yunnan Observatories Chinese Academy of Sciences 396 Yangfangwang Guandu District 650216 Kunming Peopleś Republic of China
1 shared publication - Song Feng
Kunming University of Science and Technology
1 shared publication - Wei Dai
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
1 shared publication - Yunfei Yang
Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming, China
1 shared publication
Latest Publications
- Open Access
- 19 December 2023
- Published by MDPI AG in Applied Sciences
- Vol. 14 (1) , 7
SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images
Photospheric magnetic fields are manifested as sunspots, which cover various sizes over high-resolution, full-disk, solar continuum images. This paper proposes a novel deep learning method named SIPNet, which is designed to extract and segment multiscale sunspots. It presents a new Switchable Atrous Spatial Pyramid Pooling (SASPP) module based on ASPP, employs an IoU-aware dense object detector, and incorporates a prototype mask generation technique. Furthermore, an open-source framework known as Slicing Aided Hyper Inference (SAHI) is integrated on top of the trained SIPNet model. A comprehensive sunspot dataset is built, containing more than 27,000 sunspots. The precision, recall, and average precision metrics of the SIPNet & SAHI method were measured as 95.7%, 90.2%, and 96.1%, respectively. The results indicate that the SIPNet & SAHI method has good performance in detecting and segmenting large-scale sunspots, particularly in small and ultra-small sunspots. The method also provides a new solution for solving similar problems.
- 19 December 2023
- Published by MDPI AG in Applied Sciences
- Vol. 14 (1) , 7
Open AccessSIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images
Photospheric magnetic fields are manifested as sunspots, which cover various sizes over high-resolution, full-disk, solar continuum images. This paper proposes a novel deep learning method named SIPNet, which is designed to extract and segment multiscale sunspots. It presents a new Switchable Atrous Spatial Pyramid Pooling (SASPP) module based on ASPP, employs an IoU-aware dense object detector, and incorporates a prototype mask generation technique. Furthermore, an open-source framework known as Slicing Aided Hyper Inference (SAHI) is integrated on top of the trained SIPNet model. A comprehensive sunspot dataset is built, containing more than 27,000 sunspots. The precision, recall, and average precision metrics of the SIPNet & SAHI method were measured as 95.7%, 90.2%, and 96.1%, respectively. The results indicate that the SIPNet & SAHI method has good performance in detecting and segmenting large-scale sunspots, particularly in small and ultra-small sunspots. The method also provides a new solution for solving similar problems.