一种新的沥青路面灌封裂缝自动提取方法
DOI:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

U416.2

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


A Novel Approach for Automatic Extraction Asphalt Pavement Sealed Crack
Author:
Affiliation:

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为了提高基于图像处理的沥青路面病害识别效率和精度,引入了图像增强处理中的多尺度视网膜(multi-scale retinex, MSR)算法以减弱光照不均匀、道路场景多变等因素对路面病害图像质量的影响。针对SegNet网络难以精确分割沥青路面微小病害的问题,采用比视觉几何群网络(Visual Geometry Group Network, VGG)效果更好的残差网络(Residual Network,ResNet)作为主干网络,同时加入空洞卷积(Dilation Convolution)层,提高网络对细小病害的识别性能;针对改进网络在识别病害时误检率较高的问题,运用阈值法剔除分割结果中的假阳性。为了验证改进算法的有效性,将其与具有代表性的语义分割方法(如SegNet、BiSeNet)在相同数据集上进行对比,三者的平均交并比(Mean Intersection over Union,MIoU)和F1分数(F1-score,F1)分别为(77.6%,89.9%),(67.4%,87.4%),(69.7%,89.8%)。运用提出的方法对甘肃省部分路段的路面灌封裂缝进行识别,结果与人工检测相比,漏检率为0.09%,误检率为2.49%。实验结果表明:提出方法能够更精确地提沥青路面灌封裂缝。

    Abstract:

    : In order to improve the efficiency and accuracy of asphalt pavement disease recognition based on image processing, the multi-scale retinex(MSR) algorithm belonged to image enhancement processing was utilized to reduce the factors that seriously affect pavement disease image quality, such as uneven illumination and changeable road scenes; To solve the problem that the SegNet was difficult to accurately segment the fine defects on asphalt pavement, the residual network (ResNet) with better effect than visual geometry group network (VGG) was used as the backbone network, simultaneously, the dilated convolutional layers were employed to improve the recognition performance of the improved network for small diseases; Aiming at the issues that the improved network has a high false detection rate when recognizing diseases, the threshold method was used to eliminate false positives in the segmentation results. In order to verify the effectiveness of the improved network, it was compared with the representative semantic segmentation methods (such as SegNet, BiSeNet) on the same dataset, and the mean intersection over union (MIoU) and F1 scores (F1) of the three were (77.6%, 89.9%), (67.4%, 87.4%), (69.7%, 89.8%), respectively. The proposed algorithm was used to segment asphalt pavement sealed cracks in some road sections in Gansu Province. Compared with manual detection, the missed detection rate and the false detection rate were 0.09%, 2.49%, respectively. The experimental results show that the proposed method can segment the asphalt pavement sealed cracks more accurately.

    参考文献
    相似文献
    引证文献
引用本文

邓砚学,张志华,张新秀. 一种新的沥青路面灌封裂缝自动提取方法[J]. 科学技术与工程, 2022, 22(16): 6687-6694.
Deng yanxue, Zhang zhihua, Zhang xinxiu. A Novel Approach for Automatic Extraction Asphalt Pavement Sealed Crack[J]. Science Technology and Engineering,2022,22(16):6687-6694.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-10-19
  • 最后修改日期:2022-03-07
  • 录用日期:2022-01-16
  • 在线发布日期: 2022-06-22
  • 出版日期:
×
律回春渐,新元肇启|《科学技术与工程》编辑部恭祝新岁!
亟待确认版面费归属稿件,敬请作者关注