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Smart Pothole Detection Using Deep Learning Based on Dilated Convolution.

Khaled R Ahmed1

  • 1School of Computing, Southern Illinois University, Carbondale, IL 62901, USA.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
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This study introduces efficient deep learning models for real-time pothole detection, balancing accuracy and speed. The modified VGG16 network offers improved performance for Faster R-CNN, enhancing transportation infrastructure safety.

Area of Science:

  • Computer Vision
  • Deep Learning
  • Transportation Engineering

Background:

  • Potholes pose significant risks to transportation infrastructure and economic activity.
  • Automated pothole detection using computer vision is crucial for efficient road maintenance.
  • Existing methods require optimization for accuracy, speed, and cost-effectiveness.

Purpose of the Study:

  • To develop efficient deep learning models for real-time pothole detection.
  • To improve the accuracy and speed of automated pothole detection systems.
  • To propose a cost-effective and easily implementable solution for road infrastructure monitoring.

Main Methods:

  • Development of efficient deep learning Convolutional Neural Networks (CNNs).
  • Proposal of a modified VGG16 (MVGG16) network with reduced layers and varied dilation rates.
Keywords:
YOLOv5deep learningfaster R-CNNsmart potholes detection

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  • Integration of MVGG16 as a backbone for the Faster R-CNN architecture.
  • Comparative performance analysis of YOLOv5 and Faster R-CNN with various backbones (ResNet101, ResNet50(FPN), VGG16, MobileNetV2, InceptionV3, MVGG16).

    • Main Results:

    • The YOLOv5 Small (Ys) model demonstrated superior speed for real-time pothole detection.
    • Faster R-CNN with the proposed MVGG16 backbone achieved higher mean precision and reduced inference time compared to other backbones (VGG16, InceptionV3, MobileNetV2).
    • The MVGG16 network effectively balances pothole detection accuracy and processing speed.

    Conclusions:

    • Deep learning models, particularly optimized CNNs, are highly effective for automated pothole detection.
    • The modified VGG16 backbone enhances the performance of Faster R-CNN for pothole detection tasks.
    • The proposed approach offers a promising solution for real-time, accurate, and efficient road infrastructure monitoring.