Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210900198-7.doi: 10.11896/jsjkx.210900198

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Study on Solar Radio Burst Event Detection Based on Transfer Learning

GUO Jun-cheng1, WAN Gang1, HU Xin-jie1, WANG Shuai1, YAN Fa-bao2   

  1. 1 School of Aerospace Information,Space Engineering University,Beijing 101416,China
    2 Laboratory of Space Electromagnetic Detection Technology,Shandong University,Weihai,Shandong 264200,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:GUO Jun-chen,born in 1996,postgra-duate.His main research interests include intelligent information processing and image analysis.
    WANG Shuai,born in 1992,Ph.D,lecturer.His main research interests include machine learning and space weather.

PDF (PC)

357

Abstract: Solar radio burst events are closely related to a variety of solar activities.The study of different types of radio burst events will help to improve the understanding of the physical mechanism of solar activities and strengthen the ability to interpret space weather.In order to solve the problems of small sample data,slow detection speed,low positioning accuracy and large interference of human factors in the traditional radio burst event detection methods,a small sample target detection method based on deep learning is proposed to automatically identify and locate different radio burst events in the solar radio spectrum.Firstly,due to the lack of public radio burst event detection data set,based on the radio spectrum data observed by the green bank solar radio burst spectrometer in United States,a small sample domain target detection data set with three burst types and 745 images is constructed.Then,the small sample learning method based on transfer learning is used to solve the problem of small sample data in radio burst event detection data set.Experimental results show that the proposed method is feasible and effective.

Key words: Solar radio spectrum, Object detection, Transfer learning, Few-shot learning

CLC Number: 

  • TP391.1
[1]TAN C M.Study on radio burst of solar activity and comprehen-sive analysis of multiband observation[D].Beijing:National Astronomical Observatory,Chinese Academy of Sciences,2007.
[2]LOBZIN V V,CAIRNS I H,ROBINSON P A,et al.Automatic recognition of coronal type II radio bursts:the automated radio burst identification system method and first observations[J].The Astrophysical Journal Letters,2010,710(1):L58.
[3]LOBZIN V V,CAIRNS I H,ROBINSON P A,et al.Automatic recognition of type III solar radio bursts:automated radio burst identification system method and first observations[J].Space Weather the International Journal of Research & Applications,2009,7(4):102-114.
[4]SALMANE H,WEBER R,ABED-MERAIM K,et al.A method for the automated detection of solar radio bursts in dynamic spectra[J].Journal of Space Weather and Space Climate,2018,8(6):58-69.
[5]ZHANG P J,WANG C B,YE L.A type III radio burst automa-tic analysis system and statistic results for a half solar cycle with Nanay Decameter Array data[J].Astronomy and Astrophy-sics,2018,6(4):92-108.
[6]ZHANG Q M.Research on classification and location detection methods of solar radio burst events[D].Weihai:Shandong University,2020.
[7]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Computer Society.IEEE Computer Society,2013.
[8]GIRSHICK R.Fast R-CNN[J].arXiv e-prints,2015.
[9]REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:to-wards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
[10]HUANG J,ZHANG G.Survey of object detection algorithmsfor deep convolutional neural networks[J].Computer Enginee-ring and Applications,2020,56(17):12-23.
[11]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[J].arXiv e-prints,2016.
[12]REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//IEEE Conference on Computer Vision & Pattern Recognition.IEEE,2017:6517-6525.
[13]REDMON J,FARHADI A.YOLOv3:An incremental improvement[J].arXiv e-prints,2018.
[14]LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shotmultibox detector[C]//European Conference on Computer Vision.Cham:Springer,2016.
[15]FU C Y,LIU W,RANGA A,et al.DSSD:deconvolutional single shot detector[J].arXiv e-prints,2017.
[16]HAN Y S,MA S P,HE L Y,et al.Detection of the object in the fast remote sensing airport area on the improved YOLOv3[J].Journal of Xidian University,2021,48(5):43-51.
[17]ZHAO K L,JIN X L,WANG Y Z.Survey on few-shot learning[J].Journal of Software,2021,32(2):349-369.
[18]WU T,LIU Y Q,JIANG S H.Research on SAR image ve hicle target recognition method based on transfer learning[J].Journal of Changchun University of Science and Technology(Natural Science Edition),2021,44(2):58-64.
[1] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[2] LIU Dong-mei, XU Yang, WU Ze-bin, LIU Qian, SONG Bin, WEI Zhi-hui. Incremental Object Detection Method Based on Border Distance Measurement [J]. Computer Science, 2022, 49(8): 136-142.
[3] WANG Can, LIU Yong-jian, XIE Qing, MA Yan-chun. Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization [J]. Computer Science, 2022, 49(8): 157-164.
[4] WANG Jun-feng, LIU Fan, YANG Sai, LYU Tan-yue, CHEN Zhi-yu, XU Feng. Dam Crack Detection Based on Multi-source Transfer Learning [J]. Computer Science, 2022, 49(6A): 319-324.
[5] CHEN Yong-ping, ZHU Jian-qing, XIE Yi, WU Han-xiao, ZENG Huan-qiang. Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss [J]. Computer Science, 2022, 49(6A): 424-428.
[6] CHEN Jia-zhou, ZHAO Yi-bo, XU Yang-hui, MA Ji, JIN Ling-feng, QIN Xu-jia. Small Object Detection in 3D Urban Scenes [J]. Computer Science, 2022, 49(6): 238-244.
[7] PENG Yun-cong, QIN Xiao-lin, ZHANG Li-ge, GU Yong-xiang. Survey on Few-shot Learning Algorithms for Image Classification [J]. Computer Science, 2022, 49(5): 1-9.
[8] HU Fu-yuan, WAN Xin-jun, SHEN Ming-fei, XU Jiang-lang, YAO Rui, TAO Zhong-ben. Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network [J]. Computer Science, 2022, 49(5): 10-24.
[9] XU Tao, CHEN Yi-ren, LYU Zong-lei. Study on Reflective Vest Detection for Apron Workers Based on Improved YOLOv3 Algorithm [J]. Computer Science, 2022, 49(4): 239-246.
[10] TAN Zhen-qiong, JIANG Wen-Jun, YUM Yen-na-cherry, ZHANG Ji, YUM Peter-tak-shing, LI Xiao-hong. Personalized Learning Task Assignment Based on Bipartite Graph [J]. Computer Science, 2022, 49(4): 269-281.
[11] ZUO Jie-ge, LIU Xiao-ming, CAI Bing. Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion [J]. Computer Science, 2022, 49(3): 197-203.
[12] ZHANG Shu-meng, YU Zeng, LI Tian-rui. Transferable Emotion Analysis Method for Cross-domain Text [J]. Computer Science, 2022, 49(3): 218-224.
[13] SUN Fu-quan, ZOU Peng, CUI Zhi-qing, ZHANG Kun. Classification Algorithm of Diabetic Retinopathy Based on Attention Mechanism [J]. Computer Science, 2022, 49(11A): 211000213-5.
[14] DENG Peng-fei, GUAN Zheng, WANG Yu-yang, WANG Xue. Identification Method of Maize Disease Based on Transfer Learning and Model Compression [J]. Computer Science, 2022, 49(11A): 211200009-6.
[15] CHE Ai-bo, ZHANG Hui, LI Chen, WANG Yao-nan. Single-stage 3D Object Detector in Traffic Environment Based on Point Cloud Data [J]. Computer Science, 2022, 49(11A): 210900079-6.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.