摘要
钢拱塔是钢拱斜拉桥的主要承重结构,其表观病害(如腐蚀、剥落和裂缝等)的早期检测与评估对保障桥梁结构安全至关重要。针对传统人工检测方法效率低下、主观性强且难以覆盖高空隐蔽区域的问题。提出了一种基于改进YOLOv8n-Seg深度学习框架与OSRA注意力机制的智能检测方法。利用自主研发的轮轨式检测机器人系统采集钢拱塔内部高分辨率图像数据,结合开源数据集构建了包含5 846张原始图像的多病害数据集,并通过随机裁剪、镜像翻转和亮度调整等数据增强技术将样本量扩展至23 378张图像。在算法设计层面,创新性地将OSRA注意力模块嵌入YOLOv8n-Seg网络的特征融合层,通过重叠分块策略和局部细化机制显著提升了模型对不规则边界和微小病害特征的捕捉能力。结果表明:优化后的YOLOv8-OSRA模型在独立测试集上取得了显著的性能提升,锈蚀检测mAP@0.5达到90.9%(提升2.6%),裂缝识别精度达到87.0%(提升1.1%),剥落检测准确率达到81.9%(提升2.1%)。消融实验进一步验证了OSRA模块在保持计算效率(仅增加0.8%GFLOPs)的同时,其性能优势显著优于SE和CBAM等注意力机制。研究成果为钢拱塔病害检测提供可部署于移动检测设备的轻量化解决方案,且提出的多尺度特征增强方法为复杂钢结构表面缺陷检测提供参考。
Steel arch towers,as the primary load-bearing structures of steel arch cable-stayed bridges,require early detection and assessment of their surface defects—such as corrosion,spalling,and cracks—to ensure structural safety.However,traditional manual inspection methods are inefficient,highly subjective,and unable to access concealed areas at high altitudes.To address these challenges,an intelligent detection method based on an improved YOLOv8n-Seg deep-learning framework integrated with the OSRA attention mechanism was proposed.High-resolution internal images of steel arch towers were collected using a self-developed rail-guided inspection robot system.A comprehensive dataset containing 5846 original images was constructed from both collected and open-source data,and data augmentation techniques—including random cropping,mirroring,and brightness adjustment—expanded the dataset to 23378 images.At the algorithmic level,the OSRA attention module was innovatively embedded into the feature fusion layer of the YOLOv8n-Seg network.By leveraging an overlapping patching strategy and a local refinement mechanism,the model’s ability to capture irregular boundaries and small-scale defect features was significantly enhanced.Experimental results demonstrated that the optimized YOLOv8-OSRA model achieved notable performance improvements on an independent test set:corrosion detection mAP@0.5 reached 90.9%(+2.6%),crack identification accuracy reached 87.0%(+1.1%),and spalling detection accuracy reached 81.9%(+2.1%).Ablation experiments further confirmed that the OSRA module,while maintaining computational efficiency(increasing GFLOPs by only 0.8%),outperformed conventional attention mechanisms such as SE and CBAM.The findings provided a lightweight and deployable solution for steel arch tower defect detection,and the proposed multi-scale feature enhancement approach offered valuable insights for detecting surface defects in complex steel structures.
作者
王海涵
WANG Haihan(Guangzhou Engineering Construction Command of China Railway Guangzhou Group Company Limited,Guangzhou Guangdong 510180,China)
出处
《图学学报》
北大核心
2025年第6期1327-1336,共10页
Journal of Graphics
