Structural displacement monitoring via improved YOLOv8 structure under complex scenarios

作     者：Li, M. Z. Yan, Z. T. Yang, X. G. Zhao, S. 

作者机构：Chongqing Univ Sci & Technol Sch Civil Engn & Architecture Chongqing 401331 Peoples R China Chongqing Technol & Business Univ Engn Res Ctr Waste Oil Recovery Technol & Equipmen Minist Educ Chongqing 400067 Peoples R China 

出 版 物：《STRUCTURES》 (Structures)

年 卷 期：2025年第73卷

核心收录：

基　　金：China Postdoctoral Science Foundation [2023M740420  2022M720592] 

主　　题：Displacement monitoring Complex scenarios YOLOv8 structure Feature fusion Multi-branch strategy Bytetrack algorithm 

摘      要：Accurate displacement information is of great significance for the structural safety state assessment. Due to the advantages of remote, high-resolution and easy implementation, the vision-based monitoring technologies have received widespread attention in the field of structure health monitoring. However, these are highly sensitive to lighting changes and complex scenarios in monitoring environment, which affects their further development in practical applications. Traditional monitoring methods typically rely solely on low-level appearance features to identify monitoring objects under complex scenarios. However, these features are not sufficient to achieve satisfactory accuracy, especially for low resolution images. The deep learning network based on YOLO series exhibits robust adaptability in challenging scenarios. This paper proposed an improved YOLOv8 structure for object detection under complex scenarios, where the global attention module (GAM),adaptive feature fusion (AFF),and diverse branch cross stage partial (DBCSP) modules are designed. AFF module effectively preserves the complex features of small objects in complex scenarios. DBCSP module introduces a multi-branch strategy to enhance the learning and presentation ability of object feature. Then, the state-of-the-art Bytetrack algorithm is introduced to track the detection objects for the aim of displacement extraction. Finally, the self-made dataset with circular codes and the cantilever beam with circular coded objects on its surface were used to validate the proposed structural displacement monitoring method. The dataset takes into account different complex backgrounds, scales or shooting distances, shooting angles and lighting intensities, and data augmentation techniques are used to improve the generalizability of model. The experiment shows that the improved YOLOv8 structure improves the average precision (AP) by 5.8 % on the dataset compared with YOLOv8 structure. Although the improved YOLOv8 and Y