A lightweight convolutional neural network-based model and system for defect detection and navigation on bridge road surface

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A lightweight convolutional neural network-based model and system for defect detection and navigation on bridge road surface

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

A lightweight convolutional neural network-based model and system for defect detection and navigation on bridge road surface

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/GF25-14337L" target="_blank" >GF25-14337L: Multi-fidelity modelování smykové únosnosti betonu založené na experimentech, simulaci a pravděpodobnostních přístupech (MMOSS)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ADVANCES IN ENGINEERING SOFTWARE

ISSN

0965-9978

e-ISSN

1873-5339

Svazek periodika

—

Číslo periodika v rámci svazku

208

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

21

Strana od-do

1-21

Kód UT WoS článku

001507045700001

EID výsledku v databázi Scopus

2-s2.0-105007507689