# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method  
[PERSON]  
1 School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; [EMAIL_ADDRESS] (Z.R. ); [EMAIL_ADDRESS] (C.L. ); [EMAIL_ADDRESS] (C.Z.Z)  
[PERSON]  
1 School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; [EMAIL_ADDRESS] (Z.R. ); [EMAIL_ADDRESS] (C.L. ); [EMAIL_ADDRESS] (C.Z.Z)  
[PERSON]  
3 School of Space and Environment, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS] ([PERSON], [EMAIL_ADDRESS] (Z.R. ); [EMAIL_ADDRESS] (C.L. ); [EMAIL_ADDRESS] (C.  
based on the analysis of DEMETER's observations [2]. [PERSON] et al. were the first to report the well-pronounced rising-tone QP waves accompanied by simultaneous energetic precipitations based on the CSES-01's observations, with a repetition period varying from ~1 s to ~23 s [3]. Although previous studies have achieved a certain understanding of QP emissions, its propagation features and generation mechanism are still poorly understood, due to the limitations of massive electromagnetic field data analysis. Since its launch on 2 February 2018, the CSES-01 has been operating stably in orbit for more than six years, accumulating massive electromagnetic field waveform data that contain valuable information about QP waves, providing us with new opportunities to study QP waves in depth. However, in the face of these massive electromagnetic field datasets, the traditional data processing and research methods are inefficient, and the information mining capacity is very limited. Therefore, we propose an automatic detection method to extract target information from massive electromagnetic data quickly and accurately. It is known that with the advent of intelligent technology, image recognition algorithms based on deep learning can achieve better results than traditional algorithms in areas such as target detection. This is thanks to neural networks, which can automatically extract features from large amounts of data, creating many powerful target detection algorithms. There are two main types of deep learning methods based on target recognition algorithms. One is the two-stage target detection algorithms based on the region proposal strategy, such as the Faster Region-Based Convolutional Neural Network (RCNN) algorithm [6], which has high accuracy but is slow and takes a long time to train. The second one is the one-step detection and recognition algorithms based on the regression framework. Regarding the second type, this class of algorithms first locates the object and then determines its type. They do not need to pre-identify regions where the object might be; instead, they directly predict the object's type and location. Examples include the RetinaNet algorithm [7], the YOLOv3 algorithm [8], and the CenterNet algorithm [9]. While these algorithms are fast, they have lower accuracy and perform poorly in detecting small objects. The aforementioned models require extensive prior knowledge to obtain initial frames and use non-maximum suppression to eliminate redundant predicted frames, thereby filtering out high-quality detection results. In 2020, the Facebook AI team proposed the Transformer-based end-to-end object detection algorithm DETR (Detection Transformer) [10; 11], which introduced the Transformer structure to the field of object detection and demonstrated excellent performance. However, the spectrogram feature of the QP emissions from satellite data results in different texture features and the locations of wave events vary; thus, the direct use of the existing DETR algorithm is not effective enough. Therefore, we improved the existing DETR algorithm in this work. The primary contributions of this study are listed as follows. (1) Using satellite data from the CSES-01, a dataset of 827 significant samples of QP emission events was created.