复杂场景下多类型路面病害分割方法

Multi-type pavement distress segmentation methods in complex scenarios

多类型路面病害的自动分割对于提升公路养护管理水平具有重要意义。大规模、多场景、多类型路面病害训练数据的缺乏,降低了深度学习模型在复杂场景下的泛化能力,限制了路面病害提取算法工程化应用的发展。为此,本文收集并建立了一个用于多类型路面病害分割的数据集CPCD,旨在从CCD图像中分割出高精度的路面病害。新数据集涵盖了7种常见路面病害类型,以及带有纹理相似噪声的负样本,合计6967张。在此基础上,提出了一种基于注意力机制与高分辨率网络相融合的病害分割卷积神经网络CBAM-HRNet,用于提升细小病害分割的精度。试验结果表明,本文模型在CPCD测试集上的F1、mIoU分别为91.30%、84.64%,其表现明显优于其他串联结构的主流卷积神经网络模型。研究成果将为我国公路自动化检测研究和可持续发展提供一定的支撑。

Automatic segmentation of various types of pavement distresss is of great significance for improving the level of highway maintenance management.The lack of large-scale,diverse-scenario,and multi-type training data for pavement distresss has reduced the generalization ability of deep learning models in complex scenarios,and limited the development of engineering applications for pavement distress extraction algorithms.To address this issue,this paper collects and establishes a dataset called CPCD for the segmentation of multiple types of pavement distresss,aiming to accurately segment pavement distresss from CCD images.The new dataset covers 7 common types of pavement distresss,including negative samples with texture-similar noise,totaling 6967 images.Based on this dataset,a distress segmentation convolutional neural network called CBAM-HRNet,which combines attention mechanisms with high-resolution networks is proposed to improve the accuracy of fine-grained distress segmentation.Experimental results show that the F1 score and mIoU of the proposed model on the CPCD dataset are 91.30% and 84.64% respectively.Its performance is significantly better than other mainstream convolutional neural network models with sequential structures.The research findings will provide certain support for the research on highway automation detection and sustainable development in our country.