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Journal of Intelligent Construction 2024, 2(1): 9180032 https://doi.org/10.26599/JIC.2023.9180032
Research Article | Open Access | Issue | Published: 20 February 2024

LAG-YOLO: Efficient road damage detector via lightweight attention ghost module

Show Author's Information Junxin Chena,  Xiaojie Yua,  Qiankun Lib,  Wei Wangc,  Ben-Guo Hed( )
School of Software, Dalian University of Technology, Dalian 116621, China
Department of Automation, University of Science and Technology of China, Hefei 230027, China
Guangdong–Hong Kong–Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing, Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen 518038, China
Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang 110819, China
Keywords:
deep learning, attention mechanism, road damage, lightweight detector
Cite this article:
Chen J, Yu X, Li Q, et al. LAG-YOLO: Efficient road damage detector via lightweight attention ghost module. Journal of Intelligent Construction, 2024, 2(1): 9180032. https://doi.org/10.26599/JIC.2023.9180032
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Abstract Full text About this article

Road damage detection plays an important role in ensuring road safety and improving traffic flow. The dramatic progress of artificial intelligence (AI) technology offers new opportunities for this field. In this paper, we introduce lightweight attention ghost-you only look once (LAG-YOLO), an efficient deep-learning network for road damage detection. LAG-YOLO optimizes the network structure of YOLO, making it more suitable for real-time processing and lightweight deployment while ensuring high accuracy. In addition, a novel module called attention ghost is designed to reduce the model parameters and improve the model performance by the simple attention module (SimAM). LAG-YOLO achieves an impressive parameter reduction to 4.19 million, delivering remarkable mean average precision (mAP) scores of 45.80% on the Hualu dataset and 52.35% on the RDD2020 dataset. In summary, the proposed network performs satisfactorily on extensive road damage datasets with fewer parameters, making it more suitable to be deployed in practice.


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About this article

LAG-YOLO: Efficient road damage detector via lightweight attention ghost module

Show Author's information Junxin Chena, Xiaojie Yua, Qiankun Lib, Wei Wangc, Ben-Guo Hed( )
School of Software, Dalian University of Technology, Dalian 116621, China
Department of Automation, University of Science and Technology of China, Hefei 230027, China
Guangdong–Hong Kong–Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing, Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen 518038, China
Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang 110819, China

Abstract

Road damage detection plays an important role in ensuring road safety and improving traffic flow. The dramatic progress of artificial intelligence (AI) technology offers new opportunities for this field. In this paper, we introduce lightweight attention ghost-you only look once (LAG-YOLO), an efficient deep-learning network for road damage detection. LAG-YOLO optimizes the network structure of YOLO, making it more suitable for real-time processing and lightweight deployment while ensuring high accuracy. In addition, a novel module called attention ghost is designed to reduce the model parameters and improve the model performance by the simple attention module (SimAM). LAG-YOLO achieves an impressive parameter reduction to 4.19 million, delivering remarkable mean average precision (mAP) scores of 45.80% on the Hualu dataset and 52.35% on the RDD2020 dataset. In summary, the proposed network performs satisfactorily on extensive road damage datasets with fewer parameters, making it more suitable to be deployed in practice.

Keywords: deep learning, attention mechanism, road damage, lightweight detector

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Publication history

Received: 23 October 2023
Revised: 11 November 2023
Accepted: 19 November 2023
Published: 20 February 2024
Issue date: March 2024

Copyright

© The Author(s) 2024. Published by Tsinghua University Press.

Acknowledgements

Acknowledgement

This work is funded by the National Natural Science Foundation of China (Nos. 52222810 and 62171114) and the Fundamental Research Funds for the Central Universities (No. DUT22RC(3)099).

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