SPMamba-YOLO: An Underwater Object Detection Network Based on Multi-Scale Feature Enhancement and Global Context Modeling

• URL: http://arxiv.org/abs/2602.22674v1
• Date: Thu, 26 Feb 2026 06:45:11 GMT
• Title: SPMamba-YOLO: An Underwater Object Detection Network Based on Multi-Scale Feature Enhancement and Global Context Modeling
• Authors: Guanghao Liao, Zhen Liu, Liyuan Cao, Yonghui Yang, Qi Li,
• Abstract summary: We propose a novel underwater object detection network that integrates multi-scale feature enhancement with global context modeling.<n>Experiments on the URPC2022 dataset demonstrate that the network outperforms the YOLOv8n baseline by more than 4.9% in mAP@0.5.
• Score: 12.390389688362506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 mAP@0.5, particularly for small and densely distributed underwater objects, while maintaining a favorable balance between detection accuracy and computational cost.

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