Reinforced concrete beam full response prediction with hybrid feature-orientation transformer-LSTM model

Engineering Structures, Год журнала: 2025, Номер 332, С. 120040 - 120040

Опубликована: Март 10, 2025

Язык: Английский

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Improving 2D displacement accuracy in bridge vibration measurement with color space fusion and super resolution

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103248 - 103248

Опубликована: Март 15, 2025

Язык: Английский

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Integrating crack pattern entropy measures with synthesized learners for accumulated seismic damage evaluation in reinforced concrete frames

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103271 - 103271

Опубликована: Март 26, 2025

Язык: Английский

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A Novel YOLOv10-DECA Model for Real-Time Detection of Concrete Cracks

Buildings, Год журнала: 2024, Номер 14(10), С. 3230 - 3230

Опубликована: Окт. 11, 2024

The You Only Look Once (YOLO) series algorithms have been widely adopted in concrete crack detection, with attention mechanisms frequently being incorporated to enhance recognition accuracy and efficiency. However, existing research is confronted by two primary challenges: the suboptimal performance of mechanism modules lack explanation regarding how these influence model’s decision-making process improve accuracy. To address issues, a novel Dynamic Efficient Channel Attention (DECA) module proposed this study, which designed YOLOv10 model effectiveness visually demonstrated through application interpretable analysis algorithms. In paper, dataset complex background used. Experimental results indicate that DECA significantly improves localization detection discontinuous cracks, outperforming (ECA). When compared similarly sized YOLOv10n model, YOLOv10-DECA demonstrates improvements 4.40%, 3.06%, 4.48%, 5.56% precision, recall, mAP50, mAP50-95 metrics, respectively. Moreover, even when larger YOLOv10s indicators are increased 2.00%, 0.04%, 2.27%, 1.12%, terms speed evaluation, owing lightweight design module, achieves an inference 78 frames per second, 2.5 times faster than YOLOv10s, thereby fully meeting requirements for real-time detection. These demonstrate optimized balance between tasks has achieved model. Consequently, study provides valuable insights future applications field.

Язык: Английский

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Ceramic tableware surface defect detection based on deep learning

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 141, С. 109723 - 109723

Опубликована: Дек. 5, 2024

Язык: Английский

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A Unet-inspired spatial-attention transformer model for segmenting gear tooth surface defects

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102933 - 102933

Опубликована: Окт. 1, 2024

Язык: Английский

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Sscan: Spatial and Semantic Context-Aware Network for Worker Detection at Construction Site

Опубликована: Янв. 1, 2024

Язык: Английский

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Aerial infrared thermal imaging transmission line defect detection methods incorporating explicit visual center structures

Measurement, Год журнала: 2024, Номер unknown, С. 116605 - 116605

Опубликована: Дек. 1, 2024

Язык: Английский

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