| 引用本文: | 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/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（2）：63-70 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
|
| 摘要: |
| 目的 针对水下环境复杂,水下目标因光线折射导致的目标边界模糊或外观、形状可能会发生非刚性形变,
使水下目标检测困难的问题,提出了一种基于 SimAM 注意力机制的 DCN-YOLOv5 水下目标检测方法。 方法 首
先,采用 YOLOv5 所使用的双向金字塔网络( BiFPN, Bi-directional Feature Pyramid Network) 在多个尺度上提取和
融合特征信息,从而提高目标辨别的准确度;其次,针对水下目标的外观、形状变化问题,将 C3 模块中的 CBS 模块
结合可变形卷积( DCN, Deformable Convolution Network) ,提出 DBS 模块并组成 D3 模块替换部分 C3 模块,以适应
水下目标的外观、形状变化;同时,融入加权注意力机制( SimAM) ,自适应地调节模型的关注度,进一步在复杂场景
下增强特征表达能力;最后,考虑目标边界模糊,为改善目标定位精度,采用 WIoU( Wise-IoU) 损失函数来替换交叉
熵损失,能够更好地适应不同目标类型和尺寸的特点,提高算法鲁棒性。 结果 实验结果表明:DCN-YOLOv5 可以
达到 87. 57%的平均精度( mAP ) ,检测效果优于 YOLOv5 网络和其他经典网络,平均每张图像的识别时间仅为
24. 5 ms。 结论 通过实验结果可以证明模型在检测精度明显提升的同时兼顾检测的实时性,对水下目标检测用于
实际用途有着一定的参考价值。 |
| 关键词: 水下目标检测 SimAM 注意力机制 可变形卷积 WIoU |
| DOI： |
| 分类号: |
| 基金项目: |
|
| DCN-YOLOv5 Underwater Target Detection Based on SimAM Attention Mechanism |
|
LIU Xiangju LIU Yang JIANG Shexiang
|
|
School of Computer Science and Engineering Anhui University of Science and Technology Anhui Huainan 232001
China
|
| 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. |
| Key words: underwater target detection SimAM attention mechanism deformable convolutions WioU |
