数字海洋与水下攻防2025，Vol.8Issue(4)：417-423,7.DOI:10.19838/j.issn.2096-5753.2025.04.005
基于YOLO12的轻量化水下小目标检测方法
A Lightweight Underwater Small Object Detection Method Based on YOLO12
摘要
Abstract
With the rapid development of autonomous underwater vehicle(AUV)technology,achieving high-precision and real-time detection of small underwater objects on resource-constrained platforms has become an urgent challenge.To address the performance bottlenecks of existing algorithms in underwater small object detection,a lightweight object detection method based on YOLO12 is proposed.The GhostNet module is introduced to significantly reduce parameter redundancy and a lightweight shared convolution detection head(LSCD)is employed to enhance multi-scale feature extraction.The LAMP pruning algorithm is integrated to further compress the model size while maintaining detection accuracy.Experimental results demonstrate that compared to YOLO12,our method maintains competitive accuracy while achieving average reductions of 54.34%in FLOPs,77.69%in model size,and 79.18%in parameters across both DUO and RUOD datasets.The experimental results show a strong robustness and deployment adaptability of the proposed method,effectively supporting the efficient recognition of small-scale targets by AUV,and indicating great potential for practical engineering applications.关键词
YOLO12/水下小目标检测/轻量化网络/LAMP剪枝Key words
YOLO12/underwater small object detection/lightweight network/LAMP pruning分类
信息技术与安全科学引用本文复制引用
王岳川,侯国家,马佳琦,崔宇昊..基于YOLO12的轻量化水下小目标检测方法[J].数字海洋与水下攻防,2025,8(4):417-423,7.基金项目
山东省自然科学基金"低光照条件下视觉增强与感知"(ZR2024MF125) （ZR2024MF125）
国家自然科学基金"水下图像盲复原非局部变分方法及质量评价"(61901240). （61901240）
