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HE-YOLOv5s:高效的道路缺陷检测网络

Yonghao Liu1, Minglei Duan1,2, Guangen Ding2

  • 1School of Information, Yunnan University, Kunming 650500, China.

Entropy (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种优化的YOLOv5s模型,用于检测道路缺陷,实现更快的速度和更高的准确性. 增强型号为道路维护和安全提供了更有效的解决方案.

关键词:
这是YOLOv5s.注意力模块的注意力模块.卷积神经网络是一种卷积神经网络.图像处理是图像处理的过程.道路缺陷检测检测 检测 检测 检测 检测

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 道路工程工程的道路工程.

背景情况:

  • 道路缺陷在全球范围内构成重大安全风险,导致交通事故增加.
  • 现有的道路缺陷检测模型面临着准确性和速度之间的权衡,普遍性不佳.
  • 迫切需要有效和准确的道路缺陷检测系统.

研究的目的:

  • 开发一个优化的深度学习模型,用于准确和快速的道路缺陷检测.
  • 改进YOLOv5s基准模型,以提高道路缺陷识别的性能.
  • 解决现有模型在速度,准确性和概括性方面的局限性.

主要方法:

  • 通过通过修剪和移除模块来减少参数的模型优化.
  • 实施一个改进的空间金字塔聚合快速 (SPPF) 模块,用于增强特征融合.
  • 整合了一个注意模块,专注于关键的道路缺陷特征.
  • 激活功能和采样方法的战略替代.

主要成果:

  • 与基线YOLOv5s相比,拟议的模型显示了更快的每秒 (FPS).
  • 在全球道路损坏检测挑战 (GRDDC) 数据集中,平均平均精度 (MAP) 提高了2.08%.
  • 将模型大小减少了6.07 MB,表明效率提高.
  • 优化的模型显示了改进的概括能力.

结论:

  • 优化的YOLOv5s模型有效地平衡了道路缺陷的检测速度和准确性.
  • 这些改进,包括SPPF和注意力模块,显著提高了性能.
  • 该模型为现实世界的道路缺陷监测和管理提供了可行和高效的解决方案.