拉普拉斯指导的聚变网络用于伪装物体检测
Jiangxiao Zhang1, Feng Gao1, Shengmei He1
1Xingtai University, Xingtai, HeBei, China.
Frontiers in artificial intelligence
|January 30, 2026
概括
本研究介绍了自我关联交叉关系网络 (SeCoCR),用于改进伪装物体检测 (COD). 这种新型网络有效地利用频域信息,在具有挑战性的伪装场景中增强边界检测.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 伪装物体检测 (COD) 是具有挑战性的,因为物体与背景无融合.
- 现有的频域方法难以捕获精确的对象背景边界信息.
- 需要先进的技术来改善COD的边界划分.
研究的目的:
- 提出一种新的拉普拉斯变换引导网络,用于伪装物体检测.
- 为了增强在伪装环境中捕获边界信息.
- 为了提高频域辅助COD方法的性能.
主要方法:
- 开发了自相对应交叉关系网络 (SeCoCR),利用拉普拉斯变换进行频率分析.
- 使用自我关系注意模块从低频 (原始图像) 和高频 (拉普拉斯转换) 数据中提取本地和全球特征.
- 引入了低高混合融合机制,用于整合来自两个频域的多尺度信息.
主要成果:
- 通过频域分解,SeCoCR网络有效地将语义和边界信息分开.
- 拟议的低高混合融合机制成功地整合了不同频率范围的基本特征.
- 在三个基准数据集上的实验显示,与现有的最先进方法相比,性能有了显著的改进.
结论:
- 通过利用频域分析,SeCoCR网络在伪装物体检测方面取得了重大进展.
- 拉普拉斯变换和注意力机制的整合有效地解决了先前方法中的边界信息限制.
- 这种方法在识别伪装物体及其边界方面表现出卓越的性能.
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