Background Complex underwater visual conditions cause severe missed and false detections in conventional object detection models, hindering reliable autonomous underwater exploration. This work addresses these key performance limitations. Methods We propose FSD-Net, a novel underwater detection model with two core enhancement modules. The Frequency Attention Convolution Module reduces missed detections via frequency-domain spatial feature preservation, and the Multi-dimensional Feature Enhancement Module suppresses false detections via enhanced semantic fusion. Experiments include ablation studies and state-of-the-art method comparisons on the UTDAC2020 and Brackish datasets. Results FSD-Net achieves state-of-the-art performance on both tested datasets. On the UTDAC2020 dataset, it reaches 85.7% AP50 and 82.5% F1-score, with a 3.8% AP50 improvement over the baseline model. On the Brackish dataset, it achieves 98.1% AP50 and 97.0% F1-score, with a 3.9% AP50 improvement over the baseline. The model outperforms all compared mainstream detection algorithms, and ablation studies validate the effectiveness of both proposed modules. Conclusion FSD-Net's joint frequency-spatial enhancement strategy effectively mitigates underwater image degradation challenges, providing a robust detection solution for autonomous underwater exploration. The proposed dual-module design offers a practical reference for detection model optimization in complex visual environments, with future work focused on lightweight model optimization.
Zhang et al. (Fri,) studied this question.