Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Detection method of surface damage of concrete bridge based on improved YOLOv8
In the existing target detection algorithms, it has not been improved according to the characteristics of bridge surface damages, and the detection accuracy of bridge apparent diseases under complex background is low. To enhance the accuracy of concrete bridge surface damage detection in complex backgrounds, a bridge surface damage detection method based on the improved YOLOv8 algorithm is proposed. Firstly, addressing the characteristics of densely distributed damages and significant variations in damages scales, the network structure of YOLOv8 is modified by embedding the CBAM (Convolutional Block Attention Module) attention module into the detection layer. Experimental results demonstrate that the improved YOLOv8 model exhibits significant improvements in precision, recall, average classification accuracy, and other metrics compared to the original model. The overall mean average precision increased by 1.23%, indicating a more precise and real-time detection of bridge damages.
Detection method of surface damage of concrete bridge based on improved YOLOv8
In the existing target detection algorithms, it has not been improved according to the characteristics of bridge surface damages, and the detection accuracy of bridge apparent diseases under complex background is low. To enhance the accuracy of concrete bridge surface damage detection in complex backgrounds, a bridge surface damage detection method based on the improved YOLOv8 algorithm is proposed. Firstly, addressing the characteristics of densely distributed damages and significant variations in damages scales, the network structure of YOLOv8 is modified by embedding the CBAM (Convolutional Block Attention Module) attention module into the detection layer. Experimental results demonstrate that the improved YOLOv8 model exhibits significant improvements in precision, recall, average classification accuracy, and other metrics compared to the original model. The overall mean average precision increased by 1.23%, indicating a more precise and real-time detection of bridge damages.
Detection method of surface damage of concrete bridge based on improved YOLOv8
Wu, Lijun (Herausgeber:in) / Qiu, Zhongpan (Herausgeber:in) / Zhang, Yongrui (Autor:in) / Wu, Wei (Autor:in) / Huang, Jiaqi (Autor:in) / Cong, Lee (Autor:in)
Fourth International Conference on Sensors and Information Technology (ICSI 2024) ; 2024 ; Xiamen, China
Proc. SPIE ; 13107
06.05.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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