轮信息引导的多尺度特征检测方法用于可见红外行人检测
Xiaoyu Xu1, Weida Zhan1, Depeng Zhu1
1National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
这项研究增强了红外行人检测,使用轮信息来提高准确性. 这种新的方法有效地识别了复杂场景中的行人,超过了现有的算法.
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:
- 红外行人检测面临着诸如低分辨率,差异差,复杂的背景和目标封闭等挑战.
- 这些因素导致目标特征模糊,阻碍了准确的检测.
研究的目的:
- 为了提高红外行人目标检测的准确性.
- 为应对低质量的图像和复杂的环境因素所带来的挑战.
主要方法:
- 一种预处理技术,以抑制背景噪声和提取可见图像颜色信息.
- 一个信息融合残留块,具有U形结构和残留连接,用于特征提取.
- 一个以轮信息为导向的注意力机制,用于深度特征提取.
- mIoU集群用于数据集特定的框架生成和混合损失函数以提高适应性.
主要成果:
- 拟议的方法在行人检测任务中明显优于比较算法.
- 实验结果证明了直线导向方法的优越性.
结论:
- 开发的方法有效地提高了红外行人检测的准确性.
- 轮信息指导是增强在具有挑战性的检测场景中提取特征的有希望的策略.
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