江汉大学学报（自然科学版）2026，Vol.54Issue(4)：86-96,11.DOI:10.16389/j.cnki.cn42-1737/n.2026.04.008
轻量化YOLO小目标检测算法在水下目标检测中的应用
Application of Lightweight YOLO-Based Small Object Detection Algorithm in Underwater Object Detection
摘要
Abstract
To address the problems in underwater image recognition,including weak target features,a large number of small objects with limited information,and difficulties in target recognition caused by light absorption and scattering in underwater environments,an improved algorithm,C3k2_RD-EMA-SOB-YOLOv8(CES-YOLOv8),is proposed.First,the C3k2 module is introduced to replace the original C2f module,and a lightweight RD convolution block is designed by combining re-parameterized refocusing convolution with dilated convolution,thereby reducing the computational cost of the model.Second,an efficient multi-scale attention mechanism is introduced to enhance the network's focus on key information of small objects,thereby improving small-object detection performance.Finally,a small-object detection branch is added to capture shallow feature information of small objects and fully fuse it with deep features,thus reducing the missed detection rate of small objects.Experimental results on the RUOD dataset show that,compared with the original model,the improved algorithm improves mAP@0.5 and mAP@0.5:0.95 by 1.0％and 3.3％,respectively.Compared with other cutting-edge underwater object detection algorithms,the present algorithm improves the detection accuracy while lightweighting the improvement,which effectively improves the ability to detect small underwater objects.关键词
目标检测/C3k2_RD模块/高效多尺度注意力机制/小目标检测分支Key words
object detection/C3k2_RD module/efficient multi-scale attention mechanism/small-object detection branch分类
建筑与水利引用本文复制引用
隗一凡,李浩,吴文俊,雷搏,轩亚飞,陶俊..轻量化YOLO小目标检测算法在水下目标检测中的应用[J].江汉大学学报（自然科学版）,2026,54(4):86-96,11.基金项目
江汉大学2025年研究生科研创新基金项目(KYCXXJJ2025S08) （KYCXXJJ2025S08）
