Lightweight Automatic Detection Model for Lightning Whistle Waves Based on Improved YOLOv5

This project proposes an improved YOLOv5 detection algorithm YOLOv5 Upgraded. To address this issue, the study proposes an improved YOLOv5 detection algorithm called YOLOv5-Upgraded.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 order 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 detection rate. The study is based on the VLF-band data of CSES Satellite SCM with 2.4 seconds time window 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 accuracy (mAP) of the improved YOLOv5-based model is 99.09% and the Recall is 96.20%, which are improved 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 technology lightning whistler waves recognition model is 82.89MB, while the YOLOv5-Upgraded model is only 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 features are easy to deploy to satellite devices, which greatly improves the possibility of satellite recognition.