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Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network
Zhihan Lv1, Chen Cheng2, Haibin Lv3
1Department of Game design, Faculty of Arts, 752 36 Uppsala, Uppsala University, Sweden.
Summary
This study introduces a Mask R-CNN model for efficient pavement distress identification, achieving high accuracy in detecting various crack types. This advancement aids in improving road conditions and smart city development.
Area of Science:
- Pavement engineering
- Artificial intelligence
- Computer vision
Background:
- Current methods for pavement distress identification are often inefficient and challenging.
- Accurate detection of pavement distress is crucial for road maintenance and safety.
Purpose of the Study:
- To enhance the efficiency and accuracy of automatic pavement distress identification.
- To apply deep learning techniques for recognizing road crack distress.
Main Methods:
- Analysis of pavement distress identification methods and types.
- Description of deep learning concepts for pavement distress recognition.
- Design and application of a Mask R-CNN model for road crack distress recognition.
Main Results:
- The Mask R-CNN model achieved comprehensive recognition accuracies ranging from 95% to 99%.
- Specific crack detection accuracies included: transverse cracks (95%-98%), longitudinal cracks (92%-98%), and mesh cracks (92%-98%).
Conclusions:
- The developed Mask R-CNN model offers a robust solution for pavement distress recognition.
- This research provides a reference for applying deep convolutional neural networks (CNNs) in pavement analysis and contributes to smart city initiatives.
Keywords:
longitudinal crackmask region-based convolutional neural networkmesh crackpavement distresstransverse crackMore Related Videos
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