Detection and assessment of post-earthquake functional building ceiling damage based on improved YOLOv8
Authors:
- Duozhi WangDWDuozhi Wang
- Yanzong ZhangYZYanzong Zhang
- Rong ZhangRZRong Zhang
- Guibo NieGNGuibo Nie
- Weidi WangWWWeidi Wang
- 17 November 2024
- journal article
- research article
- Published by Elsevier in Journal of Building Engineering
- Vol. 98
Abstract
No abstract availableKeywords
Funding Information
- National Key Research and Development Program of China (2019YFE0112700)
- Ministry of Science and Technology of the People's Republic of China
- China Earthquake Administration Institute of Engineering Mechanics (2023B15)
This publication has 30 references indexed in Scilit:
- The influence of torsion on acceleration demands in low-rise RC buildingsBulletin of Earthquake Engineering, 2024
- UAV-based road crack object-detection algorithmAutomation in Construction, 2023
- Road damage detection using UAV images based on multi-level attention mechanismAutomation in Construction, 2022
- Structural crack detection using deep convolutional neural networksAutomation in Construction, 2022
- A deep learning approach for fast detection and classification of concrete damageAutomation in Construction, 2021
- A real-time detection approach for bridge cracks based on YOLOv4-FPMAutomation in Construction, 2021
- Damaged ceiling detection and localization in large-span structures using convolutional neural networksAutomation in Construction, 2020
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based LocalizationInternational Journal of Computer Vision, 2019
- SSD: Single Shot MultiBox Detector2016
- Shake table tests of suspended ceilings to simulate the observed damage in the M s7.0 Lushan earthquake, ChinaEarthquake Engineering and Engineering Vibration, 2016