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Enhanced end-to-end regression algorithm for autonomous road damage detection

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Abstract

To address challenges such as variations in lighting, weather, and the size and shape of cracks and potholes, we propose an enhanced end-to-end regression algorithm for autonomous road damage detection. This method balances computational efficiency and accuracy by incorporating feature extraction structures to improve performance in scenarios involving multiple damage types, shadows, and fine-grained feature variations. The proposed model integrates a down-sampling structure for dimensionality reduction and feature extraction, an inverted residual mobile block for feature fusion, and an attention mechanism with multi-scale features for multi-scale detail extraction. Additionally, the integration of a Decoupled Head structure enhances bounding box localization. Experimental results show that the proposed method outperforms YOLOv5s (You Only Look Once version 5 small), achieving a 2.9% improvement in the F1 score and a 4% improvement in the mean average precision. Further validation through visualization experiments in seven challenging road scenarios, including varying lighting and environmental conditions, highlights the model’s superior detection accuracy, completeness, and robustness.

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The data used to support this study's findings are available from the corresponding author.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China [Grant Number 2021YFC3090303 and 2021YFC3090304]; The Fundamental Research Funds for the Central Universities (Ph.D. Top Innovative Talents Fund of CUMTB) [BBJ2024069].

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Authors and Affiliations

  1. School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, 100083, China

    Hongjia Xing, Feng Yang, Xu Qiao, Fanruo Li & Xinxin Huang

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  1. Hongjia Xing
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  2. Feng Yang
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  3. Xu Qiao
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  5. Xinxin Huang
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Contributions

HX contributed to conceptualization, data curation, investigation, methodology, validation, visualization, project administration, writing (original draft), writing (review) and editing. FY contributed to conceptualization, data curation, investigation, writing (review) and editing. XQ contributed to investigation, data curation, and project administration. FL contributed to funding acquisition, supervision, and project administration. XH contributed to data curation and supervision.

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Correspondence to Hongjia Xing.

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Xing, H., Yang, F., Qiao, X. et al. Enhanced end-to-end regression algorithm for autonomous road damage detection. J Supercomput 81, 380 (2025). https://doi.org/10.1007/s11227-024-06871-7

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