建筑钢结构进展2026，Vol.28Issue(6)：10-19,10.DOI:10.13969/j.jzgjgjz.20250805001
基于计算机视觉的钢结构螺栓病害智能检测方法研究
Research on Intelligent Detection Methods for Bolt Defects in Steel Structures Based on Computer Vision
摘要
Abstract
To address issues in steel structure bolt defect detection such as low efficiency,high missed detection and false judgment rate of manual visual inspection,poor detection accuracy,weak anti-noise capability,and large number of computational parameters of traditional deep learning models,this paper proposes a bolt defect detection model based on improved YOLOv8.The model improves the backbone using poly-scale convolution and enhances spatial feature extraction capability through multi-dilation rate convolution kernels.It introduces CSPStage and DySample modules to optimize the neck,strengthens the efficiency and quality of multi-scale feature fusion,and improves the model's anti-noise capability in complex scenarios.For dataset acquisition,DJI Mavic 4 Pro unmanned aerial vehicle was used to collect images of steel truss joint bolts from the Pingyin Yellow River Bridge.Combined with CLAHE preprocessing and data augmentation strategies to improve image data quality,a steel structure bolt defect detection dataset containing four states:rusty,loose,missing,and normal was constructed.Experimental results show that the improved YOLOv8-GDFPN model achieves a mean average precision(mAP)of 83.5%and an accuracy of 88.1%on the self-built dataset,which are 4.9%and 4.5%higher than the YOLOv8 model,respectively.The model has 3.263 M computational parameters and an FPS value of 181.8 frames per second,achieving a balance between detection accuracy and inference efficiency.This model can provide a reference for intelligent detection of bolt defects in complex outdoor scenarios.关键词
计算机视觉/螺栓病害智能检测/YOLO算法/螺栓图像采集/图像数据集构建/模型优化/抗噪能力Key words
computer vision/bolt defect intelligent detection/YOLO algorithm/bolt image acquisition/image dataset construction/model optimization/anti-noise capability分类
建筑与水利引用本文复制引用
孙博,王振,周学军,李秀领,冯帅克..基于计算机视觉的钢结构螺栓病害智能检测方法研究[J].建筑钢结构进展,2026,28(6):10-19,10.基金项目
国家自然科学基金(52278507),山东省重点研发计划(重大科技创新工程)项目(2024CXGC10321),山东省住房城乡建设科技计划(2024KYKF-JZGYH102) （52278507）
