提升水下小目标检测，融合多尺度与全局上下文建模
SPMamba-YOLO: An Underwater Object Detection Network Based on Multi-Scale Feature Enhancement and Global Context Modeling
- 设计SPPELAN模块增强多尺度特征聚合
- 在URPC2022上[email protected]提升超4.9%
- 适合水下小目标密集场景检测
水下目标检测因光照衰减、色彩失真、背景杂乱及目标尺寸小而极具挑战。为此，提出SPMamba-YOLO网络，融合多尺度特征增强与全局上下文建模。引入空间金字塔池化增强的层聚合网络（SPPELAN）模块，强化多尺度特征聚合并扩大感受野；采用金字塔分割注意力（PSA）机制，突出有效区域、抑制背景干扰。同时，集成基于Mamba的状态空间建模模块，高效捕捉长距离依赖与全局上下文信息，提升复杂水下环境下的检测鲁棒性。在URPC2022数据集上的大量实验表明，SPMamba-YOLO相较YOLOv8n基线在[email protected]上提升超过4.9%，尤其对小目标和密集分布目标表现优异，且保持检测精度与计算成本的良好平衡。
原文摘要 · Abstract (English)
Underwater object detection is a critical yet challenging research problem owing to severe light attenuation, color distortion, background clutter, and the small scale of underwater targets. To address these challenges, we propose SPMamba-YOLO, a novel underwater object detection network that integrates multi-scale feature enhancement with global context modeling. Specifically, a Spatial Pyramid Pooling Enhanced Layer Aggregation Network (SPPELAN) module is introduced to strengthen multi-scale feature aggregation and expand the receptive field, while a Pyramid Split Attention (PSA) mechanism enhances feature discrimination by emphasizing informative regions and suppressing background interference. In addition, a Mamba-based state space modeling module is incorporated to efficiently capture long-range dependencies and global contextual information, thereby improving detection robustness in complex underwater environments. Extensive experiments on the URPC2022 dataset demonstrate that SPMamba-YOLO outperforms the YOLOv8n baseline by more than 4.9\% in [email protected], particularly for small and densely distributed underwater objects, while maintaining a favorable balance between detection accuracy and computational cost.
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