空间科学学报2024，Vol.44Issue(3)：458-473,16.DOI:10.11728/cjss2024.03.2023-0067
改进YOLOv5的闪电哨声波轻量化自动检测模型
Lightweight Automatic Detection Model for Lightning Whistle Waves Based on Improved YOLOv5
摘要
Abstract
This project proposes an improved YOLOv5 detection algorithm YOLOv5 Upgraded.To address this issue,the study proposes an improved YOLOv5 detection algorithm called YOLOv5-Up-graded.The model takes into account the vector angle between the predicted edge and the real edge,The model replaces the loss function CIoU(Complete IoU)with SIoU(Scylla IoU);at the same time,in or-der to avoid phenomena such as gradient disappearance,gradient explosion,and neuron necrosis during network training,the activation function SiLU(Sigmoid-weighted Linear Unit)is replaced with Mish with better gradient flow;The CA attention mechanism is inserted into the backbone network to help the model identify the Lightning whistler waves more accurately and greatly reduce the missed detec-tion rate.The study is based on the VLF-band data of CSES Satellite SCM with 2.4 seconds time win-dow to intercept data,and 1126 time-frequency map data sets are obtained by band-pass filtering and short-time Fourier transform,and then expanded to 7882 images by image enhancement operations,of which 7091 are used as training set and 791 are used as test set.Experimentally,the average mean accu-racy(mAP)of the improved YOLOv5-based model is 99.09%and the Recall is 96.20%,which are im-proved by 2.75%and 5.07%compared with the plain YOLOv5s,and 5.89%and 9.62%compared with the time-frequency map-based YOLOv3 model.The size of LSTM based on the speech processing tech-nology lightning whistler waves recognition model is 82.89MB,while the YOLOv5-Upgraded model is on-ly 13.78 MB,saving about 83.38%of memory resources.It is shown that the model greatly reduces the leakage problem of Lightning whistler waves,achieves better results in test set,and its lightweight fea-tures are easy to deploy to satellite devices,which greatly improves the possibility of satellite recogni-tion.关键词
张衡一号卫星/闪电哨声波/YOLOv5/轻量化/自动检测Key words
CSES/Lightning whistler waves/YOLOv5/Lightweight/Automatic detection分类
天文与地球科学引用本文复制引用
路超,泽仁志玛,杨德贺,孙晓英,吕访贤,冉子霖,申旭辉..改进YOLOv5的闪电哨声波轻量化自动检测模型[J].空间科学学报,2024,44(3):458-473,16.基金项目
国家自然科学基金面上项目资助(41874174) （41874174）
