摘要
针对桥梁中的裂缝病害智能化检测,深度学习方法中DeepLabv3+分割模型因其提出了新的Encoder-Decoder结构,其融合了目标的高层语义信息与浅层特征并采用了深度分离卷积的方式,取得了优越的图像分割效果。但是,在编码模块训练过程中逐渐缩减输入数据的空间维度导致有用信息丢失,对尺度大小不一的小目标的识别带来一定的局限性。为了提高网络的分割性能,本文提出了一种基于改进DeepLabv3+的图像分割方法。通过增加的YOLOF模块与Resnet模块,进一步扩大感受野同时获取到更精确的裂缝特征图,为了验证本文改进算法的有效性,将大量实际桥梁裂缝图像作为原始数据集,将其与当前具有代表性的图像分割模型(如Mask R-CNN、DeepLabv3+)在相同数据集上进行对比。结果表明,本文算法在裂缝像素精度上相比Mask R-CNN、DeepLabv3+分别提高了12%与8%,平均像素精确度达到91.99%,平均交并比达到了81.43%,更加适用于桥梁裂缝分割任务,具有工程实际应用意义。
Crack disease is the most common disease of bridges,DeepLabv3+segmentation model puts forward a new Encoder-Decoder structure among deep learning methods.It combines the high-level semantic information and shallow features of the target,and adopts the method of deep separation and convolution,which achieves superior image segmentation effect.However,in the training process of the coding module,the spatial dimension of the input data is gradually reduced,resulting in the loss of useful information,which brings some limitations to the recognition of small targets with different scales.In order to improve the segmentation performance of the network,this paper proposes an image segmentation method based on improved DeepLabv3+.By adding Yolof module and Resnet module,the receptive field is further expanded and more accurate crack feature map is obtained at the same time.In order to verify the effectiveness of the improved algorithm,a large number of actual bridge crack images are taken as the original data set,which is compared with the current representative image segmentation models such as Mask R-CNN and DeepLabv3+on the same dataset.The results show that the algorithm in this paper improves the accuracy of crack pixels by 12%and 8%respectively compared with Mask R-CNN and DeepLabv3+.The average pixel accuracy is 91.99%,and Mean Intersection over Union is 81.43%,which is more suitable for the task of bridge crack segmentation and has practical engineering application significance.
作者
谭国金
欧吉
艾永明
杨润超
TAN Guo-jin;OU Ji;AI Yong-ming;YANG Run-chao(College of Transportation,Jilin University,Changchun 130022,China;Jilin Provincial Highway Administration,Changchun 130021,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第1期173-179,共7页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(51978309)
吉林省交通运输创新发展支撑项目(2020-1-3)。
作者简介
谭国金(1981-),男,教授,博士.研究方向:桥梁检测与加固.E-mail:tgj@jlu.edu.cn;通信作者:艾永明(1977-),男,讲师,博士.研究方向:桥梁检测与加固.E-mail:aym@jlu.edu.cn。
