التفاصيل البيبلوغرافية
| العنوان: |
An advanced method for surface damage detection of concrete structures in low-light environments based on image enhancement and object detection networks |
| المؤلفون: |
Tianyong Jiang, Lin Liu, Chunjun Hu, Lingyun Li, Jianhua Zheng |
| المصدر: |
Advances in Bridge Engineering, Vol 5, Iss 1, Pp 1-17 (2024) |
| بيانات النشر: |
SpringerOpen, 2024. |
| سنة النشر: |
2024 |
| مصطلحات موضوعية: |
Concrete structures, Surface damage detection, Low-light environments, Attention mechanism, Image enhancement, Bridge engineering, TG1-470 |
| الوصف: |
Abstract Surface damage detection in concrete structures is critical for maintaining structural integrity, yet current object detection algorithms often struggle in low-light environments. To address this challenge, this study proposed a methodology that integrates image enhancement and object detection networks to improve damage identification in such conditions. Specifically, we employ the self-calibrated illumination (SCI) model to reconstruct low-light images, which are then processed by an improved YOLOv5-based network, YOLOv5-GAM-ASFF, incorporating a global attention mechanism (GAM) and adaptive spatial feature fusion (ASFF). The performance of YOLOv5-GAM-ASFF is evaluated on a dataset of concrete structure damage images, demonstrating its superiority over YOLOv5s, YOLOv6s, and YOLOv7-tiny. The results show that YOLOv5-GAM-ASFF achieves a mAP@0.5 of 79.1%, surpassing the other models by 1.3%, 3.3%, and 5.8%, respectively. This approach provides a reliable solution for surface damage detection in low-light environments, advancing the field of structural health monitoring by improving detection accuracy under challenging conditions. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2662-5407 |
| Relation: |
https://doaj.org/toc/2662-5407 |
| DOI: |
10.1186/s43251-024-00145-1 |
| URL الوصول: |
https://doaj.org/article/54706d0e38a5438ba071e4213f3e9d87 |
| رقم الانضمام: |
edsdoj.54706d0e38a5438ba071e4213f3e9d87 |
| قاعدة البيانات: |
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