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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.
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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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The construction of masonry paving involves using materials such as bricks, stones, and concrete masonry units. These materials are chosen for their shape, color, strength, and resistance to abrasion and weathering. Masonry units can be installed dry on a thin layer of sand and a gravel base, or they can be embedded in mortar or asphalt on a concrete slab. For areas subjected to heavy vehicular loads, a rigid base layer of reinforced or unreinforced concrete is recommended. In contrast,...
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PDNet: Improved YOLOv5 Nondeformable Disease Detection Network for Asphalt Pavement.

Zhen Yang1, Lin Li1,2, Wenting Luo2

  • 1College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350108, China.

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Summary

A new lightweight algorithm, PDNet, enhances asphalt pavement disease detection efficiency by improving accuracy and speed. This method accelerates detection and ensures accurate identification of non-deformation diseases on expressways.

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate and rapid detection of non-deformation asphalt pavement diseases is crucial for expressway inspection efficiency.
  • Existing methods often face challenges in model compression, speed, and multiscale detection accuracy.

Purpose of the Study:

  • To propose a lightweight algorithm (PDNet) based on improved YOLOv5 for efficient asphalt pavement disease detection.
  • To enhance detection speed, accuracy, and robustness for multiscale conditions.

Main Methods:

  • Developed a novel cross-layer weighted cascade aggregation network (W-PAN) and economical GhostC3/ShuffleConv modules.
  • Utilized CIoU loss, K-Means++ for anchor box clustering, generative adversarial network (GAN) and Poisson fusion for data enhancement, and negative sample training (NST).
  • Implemented Softer-NMS for prediction box removal and constructed the FAFU-PD dataset.

Main Results:

  • PDNet significantly improved the F1-score by 10 percentage points on the FAFU-PD dataset compared to the original YOLOv5.
  • Achieved a 77.5% increase in frames per second (FPS), indicating substantial speed enhancement.

Conclusions:

  • PDNet offers a lightweight and efficient solution for asphalt pavement disease detection.
  • The proposed improvements lead to superior performance in terms of both accuracy and speed for expressway inspection.