A transfer learning framework for the real-time detection of atmospheric gravity waves from All-Sky Airglow Imager

Atmospheric gravity waves (AGWs) observed by the All-Sky Airglow Imager (ASAI) require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction. However, the traditional manual screening methods and existing machine learning approaches do not meet the demands of practical station monitoring, which has significantly impeded climatological statistical research based on AGWs. Therefore, a real-time detection framework for ground-based airglow gravity waves that integrates transfer learning with adaptive image preprocessing has been proposed. By employing wavelength-adaptive median filtering and multiscale fusion, the framework effectively suppresses stellar noise while preserving weak gravity wave features. The model utilizes an EfficientNet-B3 (convolutional neural network) backbone enhanced with a deformable convolutional layer, trained via a two-stage strategy: A frozen phase prevents overfitting by locking the lower level feature extractor, and a fine-tuning phase optimizes the deformable convolution through cosine annealing and layered optimization. This approach improves both feature transfer efficiency and gravity wave detection sensitivity. The resulting lightweight model achieves 91.2% accuracy with millisecond-level inference speed (23 ms per frame).