一个情境意识的多类损失函数用于语义细分,重点关注复杂的领域和类失衡
Zahra Ghanaei1, Modjtaba Rouhani2
1Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Scientific reports
|July 19, 2025
概括
我们开发了一个新的图像分割损失函数,SPix-WCE,以提高深度神经网络在不平衡数据集上的性能. 这种方法专注于复杂的图像区域,提高分段精度和关键指标,如IoU和F1-Score.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 图像细分对于机器视觉系统至关重要,但模型准确性通常受到不平衡数据集的阻碍.
- 深度神经网络 (DNN) 广泛用于图像细分,但仍在与数据不平衡作斗争.
- 提高细分精度对于整体系统性能至关重要.
研究的目的:
- 引入一种新的损失函数,SPix-WCE,旨在提高图像分割中的DNN性能.
- 通过在模型培训中专注于复杂的图像区域来解决数据不平衡.
- 通过基于超像素的权重方案来提高细分精度.
主要方法:
- 开发了SPix-WCE损失函数,使用SLIC (简单线性代集群) 算法.
- 分析超级像素以识别和聚焦复杂的图像区域.
- 实施了权重计划,以调整不同图像区域在损失计算中的影响.
- 使用三个DNN模型和四个不平衡的多类数据集进行实验.
主要成果:
- 与常用的损失函数相比,SPix-WCE损失函数表现出更高的性能.
- 在交叉与联盟 (IoU),F1-Score和像素精度指标中观察到显著的改进.
- 该方法有效地处理了不同数据集的不同程度的数据不平衡.
结论:
- 拟议的SPix-WCE损失函数有效地提高了图像分割任务中的深度神经网络性能.
- SPix-WCE为处理不平衡的图像数据集提供了一个强大的解决方案.
- 这种方法为提高机器视觉系统的准确性和可靠性提供了宝贵的进步.
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