一个新的对象检测算法将YOLOv11与双编码器特征聚合相结合
Haisong Chen1, Pengfei Yuan2, Wenbai Liu2
1School of Integrated Circuit, Shenzhen Polytechnic University, Shenzhen 518115, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究介绍了一种改进的YOLOv11双分支框架,使用RGB-D融合在具有挑战性的条件下进行强大的物体检测. 这种新的方法在低照度和遮蔽场景中提高了准确性和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 单模视觉检测在复杂的环境中扎,如低照度,遮蔽和纹理稀疏的设置.
- 现有的方法往往缺乏在多样化和具有挑战性的场景中的稳定性和概括能力.
研究的目的:
- 提出一个改进的基于YOLOv11的双分支RGB-D融合框架,以克服单模视觉检测的局限性.
- 通过整合RGB和深度信息,提高复杂场景中的对象检测性能.
- 通过多个基准数据集和配置验证框架的有效性和通用性.
主要方法:
- 一个对称的双分支架构,并行处理RGB图像和深度图.
- 集成双编码器交叉注意 (DECA) 模块用于交叉模式特征加权.
- 实现一个双编码器特征聚合 (DEPA) 模块,用于层次融合.
- 使用M3FD和VOC2007数据集的多阶段评估策略,包括RGB深度,RGB红外和单眼输入配置.
主要成果:
- 在RGB-红外模式下,在VOC2007上获得了82.59%的mAP50评分,在M3FD上获得了81.14%的mAP50评分,表现优于YOLOv11基线.
- 在M3FD上获得77.37%的mAP50与88.91%的RGB深度精度,在几何感知检测方面表现出强度.
- 废除研究证实了动态分支增强 (DBE) 和双编码器注意力 (DEA) 模块的显著贡献.
结论:
- 拟议的基于YOLOv11的双分支RGB-D融合框架显著提高了在具有挑战性的环境中对象检测的准确性和稳定性.
- 该框架在不同的模式和数据集中展示了强大的概括能力.
- 其高效且可扩展的设计为自动驾驶和机器人的高精度空间感知提供了有前途的解决方案.
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