Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Concrete dam damage detection and localisation based on YOLOv5s-HSC and photogrammetric 3D reconstruction
Abstract This paper presents a system for detecting damages in concrete dams that combines the proposed YOLOv5s-HSC algorithm and a three-dimensional (3D) photogrammetric reconstruction method to accurately identify and locate objects. Since the damages usually have complex background and blurred boundaries, Swin transformer blocks and coordinate attention modules were introduced to improve the ability of feature extraction. The mean average precision (mAP) value of the improved algorithm increased by 3.8% and exhibited a reasonably robust performance for both small objects and a considerable detection effect. Subsequently, to realize the localization of the detected damages and mapped to the corresponding positions, the projecting method was proposed by calculating the intersection of the ray from camera center and 3D photogrammetric reconstruction model generated from the same images as for detection. The results confirmed that the proposed method is appropriate for detecting damages and recording locations for intuitively exhibiting concrete dam damages.
Highlights An improved object detection algorithm called YOLOv5s-HSC is proposed. Combination of attention modules is designed and achieves well performances. Detection results are mapped on UAV based photogrammetric 3D reconstruction model. The proposed method can achieve concrete dam damages detection and localisation.
Concrete dam damage detection and localisation based on YOLOv5s-HSC and photogrammetric 3D reconstruction
Abstract This paper presents a system for detecting damages in concrete dams that combines the proposed YOLOv5s-HSC algorithm and a three-dimensional (3D) photogrammetric reconstruction method to accurately identify and locate objects. Since the damages usually have complex background and blurred boundaries, Swin transformer blocks and coordinate attention modules were introduced to improve the ability of feature extraction. The mean average precision (mAP) value of the improved algorithm increased by 3.8% and exhibited a reasonably robust performance for both small objects and a considerable detection effect. Subsequently, to realize the localization of the detected damages and mapped to the corresponding positions, the projecting method was proposed by calculating the intersection of the ray from camera center and 3D photogrammetric reconstruction model generated from the same images as for detection. The results confirmed that the proposed method is appropriate for detecting damages and recording locations for intuitively exhibiting concrete dam damages.
Highlights An improved object detection algorithm called YOLOv5s-HSC is proposed. Combination of attention modules is designed and achieves well performances. Detection results are mapped on UAV based photogrammetric 3D reconstruction model. The proposed method can achieve concrete dam damages detection and localisation.
Concrete dam damage detection and localisation based on YOLOv5s-HSC and photogrammetric 3D reconstruction
Zhao, Sizeng (Autor:in) / Kang, Fei (Autor:in) / Li, Junjie (Autor:in)
29.08.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Object Detection of UAV Images from Orthographic Perspective Based on Improved YOLOv5s
DOAJ | 2023
|Photogrammetric pavement detection system
British Library Conference Proceedings | 2008
|Rate dependent damage model for concrete - wave propagation and localisation
British Library Conference Proceedings | 1994
|