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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

420
Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
420
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

467
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.
467
Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

464
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...
464
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

480
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...
480
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

527
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
527
Workability of Concrete01:25

Workability of Concrete

398
The workability of concrete is a crucial property that affects its handling, placing, and finishing during construction. It describes the ease with which concrete can be mixed, placed, compacted, and finished. Workability is primarily concerned with the concrete's movement and its ability to resist internal friction and external resistance from molds and reinforcements during the application process.
Concrete's workability is determined by its resistance to internal forces that arise...
398

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Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
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边缘计算设备的轻量级路面裂检测模型

Zhuang Li1, Junjie Yang1, Heqi Wang2

  • 1School of Computer Science, Northeast Electric Power University, Jilin, 132012, China.

Scientific reports
|November 1, 2025
PubMed
概括
此摘要是机器生成的。

一个新的轻量级道路裂检测模型,YOLO-DGVG,使用可变形卷曲和优化的网络模块. 这种方法提高了准确性,并减少了模型大小,以有效地识别路面裂.

关键词:
适应特征提取适应特征提取增强的YOLOv8算法轻量级的模型轻量级的模型路面裂检测 路面裂检测

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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
  • 道路基础设施维护 道路基础设施维护

背景情况:

  • 道路表面裂纹检测面临诸如假阳性,错过检测和大型模型大小等挑战.
  • 现有的模型在复杂的背景和各种裂纹形状下扎.

研究的目的:

  • 提出一个轻量级和准确的道路裂检测模型,YOLO-DGVG.
  • 为了提高检测性能,同时降低计算复杂性和参数数量.

主要方法:

  • 引入可变形卷积 (DCNv2) 进入骨干网络 (C2f-DCNv2) 进行自适应形状调整.
  • 在子网络中集成轻量级GSConv和VoVGSCSP模块,以增强特征提取并降低复杂性.
  • 开发了一个分组卷积检测头 (GCH),以最大限度地减少参数体积.

主要成果:

  • 在PID数据集上进行的废弃性研究显示,回忆 (0.3-1.6%) 和mAP的增加,与YOLOv8.8相比,参数减少了22.28%.
  • 在UAPD,RDD2022和自定义数据集上的泛化实验证实了有效性.
  • 在检测任务中,YOLO-DGVG的表现优于RT-DETR和YOLOv10.
  • 在边缘设备上成功部署静态图像裂检测.

结论:

  • YOLO-DGVG在道路裂检测准确性和效率上提供了显著的改进.
  • 该模型的轻量级设计和增强的模块使其适合于现实世界的应用,包括边缘计算.