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相关概念视频

Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

486
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...
486
Lumber Defects01:23

Lumber Defects

476
Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
476
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

501
The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
501
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

476
Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
476
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

943
The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by a...
943
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

425
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
425

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YOLO11-WLBS:一种有效的模型用于路面缺陷检测.

Junqi Lin1, Pinxin Wang2, Yunkai Ruan3

  • 1College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

Scientific reports
|January 16, 2026
PubMed
概括

这项研究介绍了YOLO11-WLBS,这是一种用于智能路面缺陷检测的增强轻量级模型. 它显著提高了准确性和效率,为实时基础设施监控提供了强大的解决方案.

关键词:
检测精度的检测精度一般化 一般化 一般化轻量级的性能 轻量级的性能路面上的缺陷检测检测器在YOLO11上,你会发现YOLO11是什么意思.

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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
  • 道路基础设施工程 工程工程

背景情况:

  • 路面上的缺陷极大地影响了交通安全,耐用性和运营效率.
  • 准确和实时识别这些缺陷对于有效管理至关重要.

研究的目的:

  • 开发一个增强的轻量级模型,用于准确和实时的路面缺陷检测.
  • 通过整合新型模块来改进现有的检测框架.

主要方法:

  • 在YOLO11框架中集成了四个改进的模块:波纹转换卷积,轻量适应提取,双向特征金字塔网络和简单注意力.
  • 使用了废弃实验来验证每个模块的贡献.
  • 使用精度,回忆,F1得分和平均精度 (mAP) 评估模型性能.

主要成果:

  • YOLO11-WLBS模型实现了高性能指标:精度 (0.947),回忆 (0.895),F1得分 (0.895),mAP@0.5 (0.944) 和mAP@0.5-0.95 (0.703).
  • 与基线YOLO11相比,显著改善,包括精度增加6.4%,回忆增加15.8%.
  • 模型参数减少了25.5%,同时提高了检测准确性和效率.
  • 在具有挑战性的条件下 (极端照明,模糊) 展示了强大的性能和强大的跨数据集泛化.

结论:

  • YOLO11-WLBS为智能路面缺陷检测提供了一种高效,强大的解决方案.
  • 该模型的轻量级性质和高精度使其适合在边缘设备上实时部署.
  • 为路面维护和基础设施监控系统提供了实际潜力.