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将卷积神经网络与变压器相结合,改进YOLOv7用于多束水柱图像中的气体羽毛探测和细分
Wenguang Chen1, Xiao Wang2, Junjie Chen2
1Jiangsu Sanheng Technology Co. Ltd., Changzhou, China.
PeerJ. Computer science
|June 26, 2025
概括
这项研究通过将卷积神经网络 (CNN) 与YOLOv7模型中的变压器相结合,增强了水下气体探测. 改进的方法为在水柱图像中识别气体羽毛提供了更高的准确性和效率.
科学领域:
- 海洋地质学 海洋地质学
- 水下声学 水下声学
- 人工智能的人工智能是人工智能.
背景情况:
- 多束浴度生成高分辨率的水柱图像 (WCIs) 用于目标检测.
- 公共卫生设施中的气体羽毛由于纹理和运动稀疏而存在挑战,阻碍了传统的检测方法.
- 卷积神经网络 (CNN) 改善了检测,但与气体羽毛的延长形态作斗争.
研究的目的:
- 为了提高水下气体探测和细分的准确性和效率.
- 解决CNN在捕捉延长目标的全球背景方面的局限性.
- 改进用于WCI分析的YOLOv7模型.
主要方法:
- 开发了一种混合CNN-变压器方法,将变压器与YOLOv7.7集成在一起.
- 在骨干网络中优化了高效层聚合网络 (ELAN) 结构.
- 提出了一个新的Cross-BiFormer (C-BiFormer) 模块,用于协作本地和全球特征提取.
- 使用C-BiFormer模块构建了不同深度的网络,以增强受体场.
主要成果:
- 改进的YOLOv7模型显示了对气体雾的更高的检测和细分精度.
- 该C-BiFormer模块有效地平衡了局部特征提取与全球语义建模.
- 与基线YOLOv7.7相比,修改后的模型实现了更小的尺寸和更高的准确性.
- 在更深层的网络层中优化特征提取被证明对气柱识别更有效.
结论:
- 集成CNN和变压器为水下气体探测提供了一种优越的方法.
- 拟议的C-BiFormer模块增强了多尺度特征提取和模型效率.
- 开发的模型为分析WCIs提供了更准确,更高效的计算解决方案.
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