改进了YOLOv10:在复杂环境中实时物体检测方法.
1School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
这项研究增强了YOLOv10算法,用于检测使用Mosaic-9增强,双向特征金字塔网络 (BiFPN) 和挤压激发 (SE) 的小,封闭的物体. 改进后的模型在复杂场景中显著提高了性能.
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:
- 对小型和封闭目标的物体检测对于智能系统至关重要,但仍然具有挑战性.
- 现有的模型往往在复杂的场景中扎,限制了它们在现实世界的适用性.
研究的目的:
- 改进YOLOv10算法,以便更好地检测小物体和封闭物体.
- 为YOLO系列模型引入一个可泛化的优化框架.
主要方法:
- 实现了Mosaic-9数据增强,以增加小目标密度.
- 用双向特征金字塔网络 (BiFPN) 取代PANet,以优化特征融合.
- 集成的挤压和刺激 (SE) 将注意力引导到CSPDarknet的骨干中.
主要成果:
- 实现了69.5%的mAP@0.5,比YOLOv10n增加7.7%,推断速度为12.1毫秒.
- 马赛克-9改善了小目标感知,BiFPN增加了5.7%的mAP@0.5,SE增加了4.8%的闭塞强度.
- 在一个由6508张图像组成的自建数据集上进行的实验.
结论:
- 拟议的多模块优化框架显著提升了轻量级物体检测.
- 增强的YOLOv10算法在具有小和封闭目标的复杂场景中表现出卓越的性能.
- 这项工作有助于开发更强大,更有效的智能视觉系统.
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