WFDA: Wavelet-Based Frequency Decomposition and Aggregation for Underwater Object Detection

Abstract

Underwater Object Detection (UOD) techniques are critical for Autonomous Underwater Vehicle (AUV), which must operate in harsh underwater environments characterized by low visibility while satisfying the lightweight and real-time constraints required for vehicle-mounted systems. Current methods typically rely on underwater image enhancement combined with object detection to adapt to underwater conditions. However, these approaches mainly focus on the spatial domain, often overlooking the frequency-domain characteristics of the underwater environment. This oversight limits the removal of noise factors, such as scattering, blurring, distortion, and uneven illumination, and diminishes the focus of object edges and textures. Additionally, the increased parameter size and higher computational cost render them less suitable for real-time detection. To address these, the Wavelet-Based Frequency Decomposition and Aggregation Network (WFDA) was proposed, which leverages the Wavelet Transform (WT) to decompose features into high- and low-frequency components for effective feature modeling and fusion-based downsampling. Specifically, the Wavelet-Based Feature Decomposition Modeling (WDM) module utilized multi-level wavelet decomposition to hierarchically model features across different frequency bands, while the Wavelet-Based Feature Aggregation Downsampling (WAD) module refined and extracted core features through single-level wavelet decomposition combined with channel aggregation. Evaluations on four public datasets demonstrate that WFDA achieved state-of-the-art (SOTA) performance and efficiency, making it well-suited for real-time, high-accuracy detection on robotic platforms. Code is available at https://github.com/Mariiiiooooo/WFDA.