摘要
目前基于深度神经网络的裂缝分割模型存在着训练参数多、裂缝边缘分割粗糙、分割精度不足、缺少深度特征语义信息等问题。为解决以上问题,对分割性能较好的DeepLabv3+模型进行研究,嵌入Non-local注意力机制,并改进了主干网络ResNet101得到优化模型DeepLabv3+(N-S),最后基于优化模型的输出并使用裂缝骨架提取的方法来量化裂缝特征参数。使用的数据集为自制的混凝土梁裂缝图像数据集,并对优化前后模型作对比实验,分析了模型在各项性能上优化的有效性,并使用实测数据来验证评估裂缝各项特征参数量化方法。实验结果表明,DeepLabv3+(N-S)网络在数据集上的平均像素准确率(mean pixel accuracy,mPA)、平均交并比(mean intersection over union,mIoU)分别达到了88.86%、82.04%,较于原模型分别提高2.21%、2.54%,裂缝分割效果优于原模型,且裂缝样本各项特征参数量化的平均误差为+8.7%,低于原模型,可满足工程上的检测精度需求。
At present,the crack segmentation model based on deep neural network has problems such as many training parameters,rough edge segmentation of cracks,insufficient segmentation accuracy,and lack of deep feature semantic information.In order to solve the above problems,the DeepLabv3+model with better segmentation performance was studied,the Non-local attention mechanism was embedded and the backbone network ResNet101 was improved to obtain the optimized model DeepLabv3+(N-S),and finally based on the output of the optimized model,the crack skeleton extraction method was used to quantify the crack feature parameters.The data set used was the self-made concrete beam crack image data set,and the comparison experiments of the model before and after the optimization were carried out to analyze the effectiveness of the optimization of the model in various performances,and the measured data were used to verify the quantification method for evaluating various characteristic parameters of fractures.The experimental results show that the mean pixel accuracy(mPA)and mean intersection over union(mIoU)of the DeepLabv3+(N-S)network on the dataset have reached 88.86%and 82.04%,which are 2.21%and 2.54%higher than the original model,respectively.The crack segmentation performance is better than the original model,and the average error of quantification of various characteristic parameters of crack samples is+8.7%,which is lower than the original model,and can meet the requirements of engineering detection accuracy.
作者
张修杰
袁嘉豪
岳学军
张伟锋
ZHANG Xiu-jie;YUAN Jia-hao;YUE Xue-jun;ZHANG Wei-feng(College of Water Conservancy and Civil Engineering,South China Agricultural University,Guangzhou 510642,China;Guangdong Transportation Planning&Design Institute Group Co.,Ltd.,Guangzhou 510507,China;College of Artificial Intelligence,South China Agricultural University,Guangzhou 510642,China)
出处
《科学技术与工程》
北大核心
2023年第9期3794-3803,共10页
Science Technology and Engineering
基金
广东省级大学生创新项目(S202110564060)
交通运输部规程编制项目《公路越岭隧道水文地质勘察规程》
广东省交通规划设计研究院集团股份有限公司科技创新项目(粤交院﹝2021﹞研发YF-014,粤交院﹝2021﹞研发YF-015)。
