Addressing the issue of subpar performance in identifying shallow water marine life in underwater environments using existing methods, we propose an improved method based on the RT-DETR benchmark model. Initially, the Reparameterization Network RepViT is utilized as the backbone of the model, enhancing its feature extraction capabilities. Subsequently, a Reparameterized Parallel Dilated Convolution (RepPDC) is constructed and incorporated into the neck network, enabling the model to effectively capture long-range contextual information, thereby improving the model"s recognition accuracy. Lastly, a Bidirectional Feature Fusion Module (CAFM) is constructed based on the attention mechanism, enhancing the model"s ability to focus on key information in underwater environments. Experimental results demonstrate that the improved method significantly boosts the mAP50 to 87.5%, mAP75 to 70.9%, and mAP50:95 to 64.9%, with fewer parameters, making it a promising candidate for practical applications in the identification of shallow water marine life.