EFCWM-Mamba-YOLO: Real-Time Underwater Object Detection with Adaptive Feature Representation and Domain Adaptation

Abstract

Underwater object detection (UOD) is crucial for monitoring marine ecosystems, underwater robotics, environmental protection, and autonomous underwater vehicles (AUVs). Despite progress, many models struggle under real-world conditions due to poor visibility, dynamic lighting, and domain shifts. Traditional methods like Faster R-CNN are computationally expensive, while YOLO-based models suffer in challenging underwater scenarios. The scarcity of large-scale annotated datasets further limits model generalization. To address these challenges, we introduce UOD-SZTU-2025, a new dataset of 3,133 high-quality underwater images, sourced primarily from video platforms. The dataset is used in EFCWM (Enhanced Feature Correction and Weighting Module) to extract and refine a feature material library for detection targets. We propose EFCWM-Mamba-YOLO, a lightweight, real-time detection model designed to enhance feature representation and adapt to diverse underwater environments. The EFCWM module incorporates domain adaptation for improved robustness. Additionally, a two-stage training strategy first trains on a source domain and fine-tunes with limited target domain samples to enhance generalization. Experiments show our approach surpasses existing lightweight UOD models in accuracy, real-time performance, and robustness. Our dataset, model, and benchmark establish a strong foundation for future UOD research. The dataset for EFCWM-Mamba-YOLO is available at https://github.com/wojiaosun/UOD-SZTU-2025.

Qiang Li

Professor and header of AG

My research interests include underwater robot, collaborative robots, humanoid robots.