FSD-Net: underwater object detection based on frequency and spatial domain feature enhancement

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.