Lightning Optical Automatic Detection Method based on Deep Neural Network

In order to achieve automatic lightning image recognition, which cannot easily be handled by existing methods and still requires significant manpower consumption, we propose a lightning image dataset and a preprocessing method. At least 5 months of lightning image data is collected based on the two optical observation stations with camera, then a series of batch labeling methods is applied, which can greatly reduce the workload of subsequent manual labeling, and a dataset containing more than 30,000 labeled samples has been established. Considering that lightning varies rapidly over time, we propose a time sequence composite (TSC) preprocessing method that inputs lightning's time-varying characteristics into a model for better lightning image recognition. By using experiments, TSC is evaluated on four backbones, and it is found that this preprocess method significantly enhances the classification performance. The final trained model can successfully distinguish between "lightning" and "non-lightning" samples, and a recall rate of 86.5% and a false detection rate of 0.2% have been achieved.</p