基于改进Mask R-CNN的钢桥多病害智能识别

Intelligent Recognition of Multiple Diseases in Steel Bridges Based on Improved Mask R-CNN

钢桥在现代交通基础设施中扮演着重要的角色,然而,由于长期服役与环境影响,钢桥可能出现涂层锈蚀、螺栓脱落等病害。为解决传统的钢桥病害检测需要人工参与,费时费力且主观性强的问题,提出了一种基于深度学习的钢桥病害检智能识别方法。利用无人机在图像采集方面的优势,采集大量高清病害图像,经图像增强、标注后建立钢桥病害图像库,用于模型的训练和测试;引入掩膜区域卷积神经网络(Mask R-CNN)构建钢桥病害识别模型,实现钢桥病害的自动识别;并通过更换骨干网络的方式进一步提升模型性能。研究结果表明:将骨干网络由传统的ResNet101替换为VoVNet后模型性能显著提升,交并比阈值为0.5与0.5∶0.95时,优化后模型的识别平均精确率分别为0.84与0.59;相同交并比阈值下较之优化前有约10%的提升。将改进的模型应用于上莘桥表观病害检测,其对涂层锈蚀、螺栓锈蚀与螺栓脱落的识别准确率分别达到了89.3%、85.7%、73.1%;改进的Mask R-CNN模型在钢桥病害识别任务中表现出了优异的性能,无人机与深度学习相结合的方法能够实现钢桥病害的高精度、自动化检测,具有重要的科学研究和工程应用价值。

Steel bridges are integral to modern transportation infrastructure,but they may have structural issues such as rusting,falling bolts,and other deformations due to long-term operation and environmental factors.Traditional methods for identifying these defects are time-consuming and subjective,and they require manual intervention.To address this issue,this paper introduces an intelligent steel-bridge disease detection method utilizing deep learning.High-resolution images of affected areas on steel bridges are collected using unmanned aerial vehicles(UAVs),and these images are subsequently enhanced and annotated to establish a steel bridge disease image library for training and testing.The Mask R-CNN algorithm is employed to build a detection model,in which the backbone network is modified from ResNet101 to VoVNet for performance enhancement.The optimized model exhibits an average precision of 0.84 and 0.59 for intersection over union values of 0.5 and 0.5∶0.95,respectively,demonstrating a 10%improvement over the pre-optimization model.The application of the model to Shangxin Bridge showed detection accuracies of 89.3%,85.7%,and 73.1%for coating corrosion,bolt corrosion,and bolt detachment,respectively.The results highlight the efficacy of the refined model in steel bridge disease identification.The combination of UAV and deep learning has significant scientific and engineering value.