改进YOLOv5的闪电哨声波轻量化自动检测模型OA北大核心CSTPCD

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.