AUTOMATED ROAD DAMAGE DETECTION USING UAV IMAGES AND DEEP LEARNING

Authors

  • MR.MALLESH HATTI, KOTTE NAVYA, HEPYALA VIKASINI, PADAKANDLA AKSHAYA

Keywords:

Automated road damage detection, UAV images, deep learning, convolutional neural networks, YOLO architecture, RDD2022 dataset, infrastructure maintenance, real-time detection, image classification, object detection.

Abstract

Automated road damage detection using Unmanned Aerial Vehicle (UAV) images and deep learning techniques has emerged as a transformative approach in infrastructure maintenance. Traditional manual inspection methods are labor-intensive and prone to errors, necessitating the development of automated systems that can efficiently identify and classify road damages.

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References

Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018). Road damage detection using deep neural networks with images captured through a smartphone. arXiv preprint arXiv:1801.09454.

Zhang, L., Yang, F., Zhang, Y.D., & Zhu, Y.J. (2016). Road crack detection using deep convolutional neural network. 2016 IEEE International

Conference on Image Processing (ICIP), 3708–3712.

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Published

2023-12-24

How to Cite

MR.MALLESH HATTI, KOTTE NAVYA, HEPYALA VIKASINI, PADAKANDLA AKSHAYA. (2023). AUTOMATED ROAD DAMAGE DETECTION USING UAV IMAGES AND DEEP LEARNING. Pegem Journal of Education and Instruction, 13(4), 538–544. Retrieved from https://www.pegegog.net/index.php/pegegog/article/view/4017

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

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