ZH-1 satellite has accumulated substantial data of Lightning Whistlers(LWs)over its six years in orbit,serving as crucial tools for comprehensive study of the space physical environment and inter-layer coupling mechanisms.However,the current algorithms require decades to identify LWs,which is impractical for engineering applications.To address this,we propose a fast and efficient Lightweight Network(LW-LWNet)for detecting lightning whistlers.Our approach utilizes lightweight technologies such as depth-separable convolution and squeeze excitation mechanism to enhance the backbone network of YOLOv5 target detection algorithm.This reduces parameters and computational complexity,thereby improving inference speed.Additionally,we employ a small computational attention mechanism to improve the backbone network's output channels,highlighting the characteristics of lightning whistle waves and mitigating performance degradation due to parameter compression.The LW-LWNet model was trained and evaluated on LW datasets from September 2019,achieving an accuracy of 88.8％,a recall of 80.6％,a precision of 89.8％,and an F1 score 89.3％.These results represent improvements of 0.7％,0.9％,0.4％,and 0.6％respectively over the original algorithm.Furthermore,the model's parameters were reduced by 57％,inference speed(FPS)increased by 33％,and detection accuracy(mAP50)improved by 0.3％.Experiments demonstrate that the LW-LWNet model not only enhances recognition accuracy but also significantly boosts inference speed,offering an effective reference for exploring the temporal and spatial distribution of global lightning whistlers.
维普
万方数据
