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BSEFNet:基于边缘特征深度融合的双向自我注意边缘融合网络突出物体检测

Gan Gao¹, Yuanyuan Wang¹, Feng Zhou¹

¹School of Information Technology, Yancheng Institute of Technology, Yancheng, JiangSu, China.

PeerJ. Computer science

|February 3, 2025

概括

这项研究引入了一种用于突出物体检测的新方法,通过融合目标和边缘信息来提高边界精度. 这种新的方法增强了细节丰富的预测,在基准数据集上表现优于现有模型.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
图像处理图像处理

背景情况:

深度学习模型,特别是完全卷积网络 (FCN),被广泛用于像素级突出性预测.
现有的突出物体检测算法由于有限的边缘信息利用,往往难以准确的界限划分.

研究的目的:

为了提高突出目标检测的边界精度.
通过整合边缘和目标特征来提高突出物体检测模型的整体性能.

主要方法:

提出了一种新的方法,整合了突出的目标和边缘信息.
引入了一个自我注意的组像素融合模块 (SGPFM) 用于特征提取和优化.
开发了一种双向特征融合模块 (BFF) 用于渐进的特征融合和增强.

主要成果:

拟议的模型显著提高了目标边界的检测精度.
实验结果显示,在四个基准数据集中,与最先进的方法相比,性能优越.
该模型提供了准确而详细的突出目标预测.

结论:

突出目标和边缘信息的整合有效地解决了边界划分的挑战.
新的SGPFM和BFF模块增强了功能互补性,并优化了预测.
这一进步有助于更精确和详细的突出物体检测.

关键词:

在 BFF BFF 里面.深度学习是一种深度学习.边缘信息边缘信息在SGPFMFM中,您可以使用SGPFM.突出物体检测突出物体检测

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