LBSD-YOLO: A Lightweight YOLOv10-Based Network with Multi-Attention Enhancement for Bridge Surface Defect Detection

Rengdong Ji; Yunlong Xu; Xiaoyan Wang; Liyun Zhuang; Xiaojun Zhang; Xiu Tang; Jiaxin Shi

doi:10.62762/TSCC.2025.718989

Article Information

Published in ICCK Transactions on Sensing, Communication, and Control

Volume/Issue Volume 3, Issue 1, 2026

Pages 39-53

Abstract

Bridge surface defect detection plays a critical role in ensuring traffic safety and facilitating infrastructure maintenance. A lightweight object detection network based on YOLOv10, termed LBSD-YOLO, is developed to achieve high detection accuracy while maintaining high efficiency for deployment on resource-constrained devices. The proposed framework consists of three main components: a feature extraction backbone, a feature fusion neck, and a detection head. In the backbone, the C2f\_FEMA (C2f with Feature Enhancement and Multi-branch Attention) module and the LAEDS (Lightweight Adaptive Encoder–Decoder for Sampling) spatial attention module are incorporated to enhance multi-scale feature representation.The neck incorporates multi-scale feature fusion with an Efficient Multi-scale Attention (EMA) mechanism. In the detection head, a lightweight DP-Head structure is developed, variant integrated with the DAMF\_CA coordinate attention for improved channel and spatial focus. Experiments are conducted on the self-built BDD-1234 dataset, which contains 6,617 high-resolution images covering six common bridge defect categories (cracks, spalling, exposed reinforcement, rust stains, efflorescence, and delamination). Compared to the baseline YOLOv10s, LBSD-YOLO reduces model size from 16.6 MB to 9.6 MB (42.2% reduction), computational complexity from 21.4 GFLOPs to 17.3 GFLOPs (19.2% reduction), and parameters from 7.2 M to 4.6 M (36.1% reduction), while achieving comparable detection performance (mAP@50 of 64.1% vs. 65.5%). The results demonstrate that LBSD-YOLO offers an efficient and accurate solution for real-time bridge defect detection on portable devices.

Graphical Abstract

LBSD-YOLO: A Lightweight YOLOv10-Based Network with Multi-Attention Enhancement for Bridge Surface Defect Detection

Keywords

bridge surface defect detection lightweight object detection LBSD-YOLO

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

References

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Cite This Article

APA Style

Ji, R., Xu, Y., Wang, X., Zhuang, L., Zhang, X., Tang, X., & Shi, J. (2026). LBSD-YOLO: A Lightweight YOLOv10-Based Network with Multi-Attention Enhancement for Bridge Surface Defect Detection. ICCK Transactions on Sensing, Communication, and Control, 3(1), 39–53. https://doi.org/10.62762/TSCC.2025.718989

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TY  - JOUR
AU  - Ji, Rengdong
AU  - Xu, Yunlong
AU  - Wang, Xiaoyan
AU  - Zhuang, Liyun
AU  - Zhang, Xiaojun
AU  - Tang, Xiu
AU  - Shi, Jiaxin
PY  - 2026
DA  - 2026/03/10
TI  - LBSD-YOLO: A Lightweight YOLOv10-Based Network with Multi-Attention Enhancement for Bridge Surface Defect Detection
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 3
IS  - 1
SP  - 39
EP  - 53
DO  - 10.62762/TSCC.2025.718989
UR  - https://www.icck.org/article/abs/TSCC.2025.718989
KW  - bridge surface defect detection
KW  - lightweight object detection
KW  - LBSD-YOLO
AB  - Bridge surface defect detection plays a critical role in ensuring traffic safety and facilitating infrastructure maintenance. A lightweight object detection network based on YOLOv10, termed LBSD-YOLO, is developed to achieve high detection accuracy while maintaining high efficiency for deployment on resource-constrained devices. The proposed framework consists of three main components: a feature extraction backbone, a feature fusion neck, and a detection head. In the backbone, the C2f\_FEMA (C2f with Feature Enhancement and Multi-branch Attention) module and the LAEDS (Lightweight Adaptive Encoder–Decoder for Sampling) spatial attention module are incorporated to enhance multi-scale feature representation.The neck incorporates multi-scale feature fusion with an Efficient Multi-scale Attention (EMA) mechanism. In the detection head, a lightweight DP-Head structure is developed, variant integrated with the DAMF\_CA coordinate attention for improved channel and spatial focus. Experiments are conducted on the self-built BDD-1234 dataset, which contains 6,617 high-resolution images covering six common bridge defect categories (cracks, spalling, exposed reinforcement, rust stains, efflorescence, and delamination). Compared to the baseline YOLOv10s, LBSD-YOLO reduces model size from 16.6 MB to 9.6 MB (42.2% reduction), computational complexity from 21.4 GFLOPs to 17.3 GFLOPs (19.2% reduction), and parameters from 7.2 M to 4.6 M (36.1% reduction), while achieving comparable detection performance (mAP@50 of 64.1% vs. 65.5%). The results demonstrate that LBSD-YOLO offers an efficient and accurate solution for real-time bridge defect detection on portable devices.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Ji2026LBSDYOLO,
  author = {Rengdong Ji and Yunlong Xu and Xiaoyan Wang and Liyun Zhuang and Xiaojun Zhang and Xiu Tang and Jiaxin Shi},
  title = {LBSD-YOLO: A Lightweight YOLOv10-Based Network with Multi-Attention Enhancement for Bridge Surface Defect Detection},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2026},
  volume = {3},
  number = {1},
  pages = {39-53},
  doi = {10.62762/TSCC.2025.718989},
  url = {https://www.icck.org/article/abs/TSCC.2025.718989},
  abstract = {Bridge surface defect detection plays a critical role in ensuring traffic safety and facilitating infrastructure maintenance. A lightweight object detection network based on YOLOv10, termed LBSD-YOLO, is developed to achieve high detection accuracy while maintaining high efficiency for deployment on resource-constrained devices. The proposed framework consists of three main components: a feature extraction backbone, a feature fusion neck, and a detection head. In the backbone, the C2f\\_FEMA (C2f with Feature Enhancement and Multi-branch Attention) module and the LAEDS (Lightweight Adaptive Encoder–Decoder for Sampling) spatial attention module are incorporated to enhance multi-scale feature representation.The neck incorporates multi-scale feature fusion with an Efficient Multi-scale Attention (EMA) mechanism. In the detection head, a lightweight DP-Head structure is developed, variant integrated with the DAMF\\_CA coordinate attention for improved channel and spatial focus. Experiments are conducted on the self-built BDD-1234 dataset, which contains 6,617 high-resolution images covering six common bridge defect categories (cracks, spalling, exposed reinforcement, rust stains, efflorescence, and delamination). Compared to the baseline YOLOv10s, LBSD-YOLO reduces model size from 16.6 MB to 9.6 MB (42.2\% reduction), computational complexity from 21.4 GFLOPs to 17.3 GFLOPs (19.2\% reduction), and parameters from 7.2 M to 4.6 M (36.1\% reduction), while achieving comparable detection performance (mAP@50 of 64.1\% vs. 65.5\%). The results demonstrate that LBSD-YOLO offers an efficient and accurate solution for real-time bridge defect detection on portable devices.},
  keywords = {bridge surface defect detection, lightweight object detection, LBSD-YOLO},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}

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