CHGAFF-YOLO: A Cascade Hybrid Global Adaptive Feature Fusion Framework for Real-Time Ocean Internal Wave Detection

Abstract

Internal waves (IWs) are widely present in the global ocean and play a crucial role in ocean dynamics, material transport, and climate change research. However, due to the large-scale variations and complex morphological features of IW objects, existing detection methods have limitations in cross-scale feature extraction and information fusion. To address the aforementioned issues, this letter proposes a cascade hybrid global adaptive feature fusion you only look once (CHGAFF-YOLO) model for ocean IW detection. First, we employ the HGNetV2 structure as the backbone network of YOLO to better capture global information and extract complex features in a lightweight manner. Second, we introduce a Cascade Hybrid Cross-head Attention Module (CHCAM), which integrates in-group cascade attention with cross-head parallel self-attention mechanisms to achieve optimized multiscale feature extraction and enhance global feature representation capability. Finally, we design a Four-Head Adaptive Feature Fusion Module (FHAFF), which dynamically fuses feature information from different scales by constructing four detection heads, further enhancing cross-scale information interaction. Extensive experimental results show that our method significantly outperforms existing approaches on both the Sentinel-1 synthetic aperture radar (SAR) and MODIS satellite IW remote sensing datasets.