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Road surface damage detection is essential for effective infrastructure management and maintenance. This project explores the use of deep learning techniques for identifying and classifying various types of road surface damage, such as cracks, potholes, and deformations, from image data. By utilizing high-quality datasets and advanced preprocessing methods, the system aims to enhance detection accuracy and robustness under diverse environmental conditions. The approach leverages deep learning models to automatically extract and analyze features, enabling precise damage identification without reliance on manual inspection. This work seeks to improve the efficiency and scalability of road maintenance processes while ensuring reliable detection outcomes.

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Ensemble Approach For Surface Road Damage Detection Using Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • G. Balu Narasimha Rao,
  • Venkata Sai Sudheer Kumar Batchu,
  • Kesava Sai Manikanta Mandela,
  • John Lily Joel Vunnamatla

摘要

Road surface damage detection is essential for effective infrastructure management and maintenance. This project explores the use of deep learning techniques for identifying and classifying various types of road surface damage, such as cracks, potholes, and deformations, from image data. By utilizing high-quality datasets and advanced preprocessing methods, the system aims to enhance detection accuracy and robustness under diverse environmental conditions. The approach leverages deep learning models to automatically extract and analyze features, enabling precise damage identification without reliance on manual inspection. This work seeks to improve the efficiency and scalability of road maintenance processes while ensuring reliable detection outcomes.