重庆工商大学学报（自然科学版）2025，Vol.42Issue(2)：63-70,8.DOI:10.16055/j.issn.1672-058X.2025.0002.009
基于SimAM注意力机制的DCN-YOLOv5水下目标检测
DCN-YOLOv5 Underwater Target Detection Based on SimAM Attention Mechanism
摘要
Abstract
Objective Given the complex underwater environment,the target boundary may be blurred or the appearance and shape of the underwater target may be non-rigidly deformed due to light refraction,which makes underwater target detection difficult.A DCN-YOLOv5 underwater target detection method based on the SimAM attention mechanism was proposed.Methods Firstly,the bi-directional feature pyramid network(BiFPN)used by YOLOv5 was used to extract and fuse feature information on multiple scales to improve the accuracy of target recognition.Secondly,to address the variations in appearance and shape of underwater objects,the CBS module in the C3 module was combined with the deformable convolution network(DCN),and the DBS module was proposed.The DBS module was used to form the D3 module and replace part of the C3 module to adapt to the changing appearance and shape of the underwater targets.At the same time,the weighted attention mechanism was integrated to adaptively adjust the attention of the model and further improve the feature expression ability in complex scenes.Finally,considering the fuzzy boundary of the target and to improve the target positioning accuracy,the WIoU(Wise-IoU)loss function was used to replace the cross-entropy loss,which can better adapt to the characteristics of different target types and sizes and improve the robustness of the algorithm.Results Experimental results showed that DCN-YOLOv5 achieved an average precision(mAP)of 87.57％,outperforming YOLOv5 and other classical networks,with an average identification time of only 24.5 ms per image.Conclusion The experimental results demonstrate that the model significantly improves detection accuracy while ensuring real-time detection,providing valuable insights for the practical use of underwater target detection.关键词
水下目标检测/SimAM注意力机制/可变形卷积/WIoUKey words
underwater target detection/SimAM attention mechanism/deformable convolutions/WioU分类
计算机与自动化引用本文复制引用
刘向举,刘洋,蒋社想..基于SimAM注意力机制的DCN-YOLOv5水下目标检测[J].重庆工商大学学报（自然科学版）,2025,42(2):63-70,8.基金项目
安徽省重点实验室项目(ZKSYS202204). （ZKSYS202204）
