协作伪装物体检测:一个大规模的数据集和基准
IEEE transactions on neural networks and learning systems
|October 27, 2023
概括
本研究介绍了协作伪装物体检测 (CoCOD) 和CoCOD8K数据集. 拟议的BBNet模型通过探索图像间和图像内线索,有效地检测伪装物体.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 伪装物体检测 (COD) 是具有挑战性的,因为物体与背景混合.
- 现有的方法往往侧重于单个图像,缺乏协作背景.
- 需要一个大规模的数据集,用于协作伪装物体检测.
研究的目的:
- 引入一个新的任务:协作伪装物体检测 (CoCOD).
- 提交了第一份关于 CoCOD. 的大规模数据集 (CoCOD8K).
- 为 CoCOD.提出一个新的基线模型 (BBNet).
主要方法:
- 构建了CoCOD8K数据集,包含8528张图像,跨越多种伪装场景.
- 开发了BBNet与图像间协作特征探索 (CFE) 和图像内部对象特征搜索 (OFS) 模块.
- 集成了一个局部-全球精制 (LGR) 模块,用于增强检测.
主要成果:
- 在CoCOD8K数据集上对18个最先进的模型进行了基准测试.
- 与现有的COD和CoSOD算法相比,BBNet表现出明显优异的性能.
- 广泛的实验验证了拟议的BBNet模型的有效性.
结论:
- CoCOD8K数据集和BBNet模型为COCOD研究提供了坚实的基础.
- 拟议的方法有效地利用协作信息来改进伪装物体检测.
- 这项工作旨在推动伪装物体检测领域的发展.
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