MED-YOLOv8s: a new real-time road crack, pothole, and patch detection model-Reference-Cited by-同舟云学术

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MED-YOLOv8s: a new real-time road crack, pothole, and patch detection model

Published:2024-01-29 Issue:2 Volume:21 Page:
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Zhao Minghu,Su Yaoheng,Wang Jiuxin,Liu Xinru,Wang Kaihang,Liu Zishen,Liu Man,Guo Zhou

Funder

XIAN Youth Talent Support Program

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11554-023-01405-5.pdf

Reference64 articles.

1. Hou, Y., Li, Q., Zhang, C., et al.: The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis. Engineering 7(6), 845–856 (2021)

2. Pais, J.C., Amorim, S.I.R., Minhoto, M.J.C.: Impact of traffic overload on road pavement performance. J. Transp. Eng. 139(9), 873–879 (2013)

3. Madli, R., Hebbar, S., Pattar, P., et al.: Automatic detection and notification of potholes and humps on roads to aid drivers. IEEE Sens. J. 15(8), 4313–4318 (2015)

4. Gao, Y., Cao, H., CAI, W., et al.: Pixel-level road crack detection in UAV remote sensing images based on ARD-Unet. Measurement 219 (2023)

5. Rojo, M., Gonzalo-Orden, H., Linares, A., et al.: Impact of a lower conservation budget on road safety indices. J. Adv. Transp. 2018, 1–9 (2018)

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2. YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism;Journal of Real-Time Image Processing;2024-08-31

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4. YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness;2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS);2024-04-18

5. An End-to-End Platform for Managing Third-Party Risks in Oil Pipelines;IEEE Access;2024

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