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MFF-YOLO:基于多尺度特征融合的精确模型来检测道缺陷.

Anfu Zhu1, Bin Wang1, Jiaxiao Xie1

  • 1School of Electronic Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括

一个新的MFF-YOLO模型增强了道层检查. 这种先进的卷积神经网络提高了检测准确性和可靠性,确保更安全,更持久的道.

科学领域:

  • 土木工程 土木工程是指土木工程.
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 在确保道的安全性和寿命方面,对道面的常规检查至关重要.
  • 现有的检测方法可能会遇到诸如重复检测和错过目标等问题.

研究的目的:

  • 开发一个改进的卷积神经网络模型,用于增强道层检测.
  • 解决当前模型中特征学习效率和检测精度的局限性.

主要方法:

  • 开发了MFF-YOLO模型,在子中整合了一个多尺度的特征融合网络.
  • 在预测阶段实施重权选方法,以减少重复检测.
  • 调整损失函数以优化模型训练和整体性能.

主要成果:

  • 多年财政框架-YOLO模型的回忆率和准确率分别为89.5%和89.4%.
  • 与YOLOv5模型相比,显著改善,回忆力和准确性分别增加了7.1%和6.0%.
  • 成功识别的目标错过或错误地被以前的模型检测到,提高总体检测可靠性.

结论:

  • 与现有方法相比,MFF-YOLO模型在道层检测方面提供了卓越的性能.
关键词:
深度学习是一种深度学习.功能融合功能融合功能多个尺度的多个尺度目标检测 目标检测 目标检测

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  • 多尺度特征融合和重权选的整合提高了检测准确性并减少了错误.
  • 这一进步有助于更有效,更可靠的道安全和维护协议.