A lightweight convolutional neural network-based model and system for defect detection and navigation on bridge road surface
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F26%3A0200224" target="_blank" >RIV/00216305:26110/26:0200224 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0965997825001103?pes=vor&utm_source=clarivate&getft_integrator=clarivate" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0965997825001103?pes=vor&utm_source=clarivate&getft_integrator=clarivate</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.advengsoft.2025.103972" target="_blank" >10.1016/j.advengsoft.2025.103972</a>
Alternative languages
Result language
angličtina
Original language name
A lightweight convolutional neural network-based model and system for defect detection and navigation on bridge road surface
Original language description
The Faster Region-based Convolutional Neural Network (Faster R-CNN) is widely used for detecting defects on road surface. However, its effectiveness in this task is limited by its large model size and slow detection speed. To address these challenges, two versions of the Faster R-CNN model-small and large-were developed. First, the models were structurally optimized by integrating inverted residual blocks, depthwise separable convolutions, and attention mechanisms to improve efficiency and performance. The large version also incorporated multiscale feature extraction for enhanced detection capabilities. Second, model pruning was applied to further compress the networks. Extensive ablation experiments were conducted to investigate the relationship between the model's internal structure and its impact on crack detection accuracy and efficiency. The experimental results demonstrate that the proposed models outperform general CNN-based models in bridge surface defect detection, achieving superior detection speed while maintaining high accuracy. The large version exhibits better performance but at the cost of increased model complexity. Testing was conducted on a real-life bridge in Nanjing, China. Additionally, a software application, integrated with a laptop and a smartphone, was deployed to identify defects and map their locations on the bridge, streamlining the detection process. The source code of this software is freely available at https://github.com/DUYA686686/detection-software.git
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
<a href="/en/project/GF25-14337L" target="_blank" >GF25-14337L: Multi-fidelity modelling of shear concrete strength based on experiments, simulation and probabilistic approaches (MMOSS)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ADVANCES IN ENGINEERING SOFTWARE
ISSN
0965-9978
e-ISSN
1873-5339
Volume of the periodical
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Issue of the periodical within the volume
208
Country of publishing house
GB - UNITED KINGDOM
Number of pages
21
Pages from-to
1-21
UT code for WoS article
001507045700001
EID of the result in the Scopus database
2-s2.0-105007507689