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相关概念视频

Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

267
Concrete pavement joints are essential for maintaining the structural integrity and longevity of pavement by controlling where and how the pavement cracks. These joints can be categorized based on their functions, such as contraction or control joints, construction joints, isolation joints, and expansion joints.
Contraction joints are typically formed by sawing a groove into the concrete shortly after it has hardened. This creates a weakened vertical plane, deliberately encouraging cracking at...
267

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相关实验视频

Updated: Sep 13, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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一个轻量级的路面缺陷检测算法,集成感知增强和功能优化.

Xiang Zhang1, Xiaopeng Wang1, Zhuorang Yang1

  • 1School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Sensors (Basel, Switzerland)
|July 30, 2025
PubMed
概括
此摘要是机器生成的。

一个新的轻量级路面缺陷检测算法,PGS-YOLO,提高了准确性,减少了计算. 这种先进的模型提高了路面检查效率和实时性能.

关键词:
细节增强的卷积细节增强的卷积动态上抽样方法.轻量级的轻量级的轻量级的轻量级的路面上的缺陷检测,路面上的缺陷检测.接收性场所引导的注意力.

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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
  • 土木工程 土木工程是指土木工程.

背景情况:

  • 路面缺陷检测模型面临着大型计算的挑战,并与精度平衡复杂性.
  • 现有的方法难以有效地提取和融合多尺度特征,以准确识别缺陷.

研究的目的:

  • 开发一种轻量级路面缺陷检测算法,PGS-YOLO,可以克服计算限制并提高检测准确性.
  • 增强特征提取和融合能力,以更有效地识别路面缺陷.

主要方法:

  • 基于YOLOv8的提议PGS-YOLO算法,集成感知增强和功能优化.
  • 为感知增强特征提取网络 (PEFNet) 设计了RFCBAMConv和C2f-RFCBAM,以改进多级特征提取.
  • 引入了DySample用于通用化动态采样特征金字塔网络 (GDSFPN),以优化特征融合.
  • 使用共享细节增强的卷积轻量检测头 (SDCLD) 和Wise-IoU来提高性能.

主要成果:

  • PGS-YOLO在GRDDC2022数据集及其中国子集上分别实现了2.8%和2.9%的mAP50增加.
  • 与YOLOv8n相比,参数减少了10.3%,计算减少了9.9%,率为69 FPS.
  • 在CRACK500数据集上,mAP50得到了2.3%的改进,显示出复杂性和准确性之间的更好的平衡.

结论:

  • PGS-YOLO为路面缺陷检测提供了模型复杂性和检测准确性之间的卓越平衡.
  • 该算法提供了高效的实时性能,适用于实际路面检查应用.
  • 提议的特征提取和融合方面的改进有助于在路面缺陷检测方面取得最先进的结果.