Ocean internal waves (IWs), induced by density stratification and fluid perturbations, are significant oceanic phenomena prevalent across global oceans, profoundly impacting marine environments and engineering safety. Although one-stage object detection models are favored in practical applications due to their efficient inference, they often suffer from insufficient accuracy in IW detection tasks. To address this, we introduce a novel one-stage, anchor-free detection approach based on Transformer for IW detection, proposing a new algorithm named IW-D-FINE, which balances detection accuracy and inference efficiency. On the public SAR dataset, IW-D-FINE achieves an AP@0.5 of 90.5, significantly outperforming existing one-stage methods while maintaining faster inference speeds than mainstream two-stage models. Furthermore, to mitigate the scarcity of internal wave samples, we construct a small-scale IWs dataset, YH3-IW-2025, and validate the algorithm thoroughly on this dataset. Experimental results demonstrate that IW-D-FINE exhibits robust performance under complex background interference, highlighting its application potential and scalability in IW detection tasks.