动态边缘对比学习用于在长尾声纳图片中开放式识别.
Yu Lin1, Shuiyuan He2, Weidong Luo1
1Guangzhou Marine Geological Survey, Guangzhou, Guangdong, China.
Scientific reports
|July 2, 2025
概括
动态边缘对比学习 (DMCL) 通过解决数据不平衡和未知类来增强声纳图像分类. 这种新的框架在具有挑战性的识别任务中提高了准确性和稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 声纳图像分类面临着长尾分布和开放集识别的挑战.
- 现有的方法很难有效地处理不平衡的数据集,并识别未知的类.
研究的目的:
- 引入动态边缘对比学习 (DMCL),这是一个用于声纳图像分类的新框架.
- 为了同时应对长尾分布和开放集识别的挑战.
- 为了提高声纳图像分类模型的稳定性和准确性.
主要方法:
- 对于自适应式学习,DMCL使用基于类频率的动态边缘机制.
- 采用对比式学习策略来生成强大的特征表示.
- 包含一个不确定性估计模块,以有效检测未知类.
主要成果:
- 与现有方法相比,DMCL在NKSID声纳图像数据集上表现出优异的性能.
- 在宏观F1 (89.47%),规范精度 (81.90%),OSCRmac (91.01%) 和OSFM (93.21%) 中取得了显著的改进.
- 在宏观F1中表现比PLUD方法高5.79%,在OSFM中表现比PLUD方法高5.87%.
结论:
- 在声纳图像分类中,DMCL有效地处理长尾分布和开放集识别.
- 该框架在数据不平衡和未知的类场景领域提供了潜在的应用.
- 通过对基准声纳数据集的全面实验结果验证有效性.
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