基于改进U型神经网络的路面裂缝检测方法

惠冰; 李远见

doi:10.3963/j.jssn.1674-4861.2023.01.011

基于改进U型神经网络的路面裂缝检测方法

doi: 10.3963/j.jssn.1674-4861.2023.01.011

惠冰^{1, 2,},
李远见³

1.
长安大学公路学院西安 710064
2.
长安大学民航机场智慧建造与维养重点实验室西安 710064
3.
内蒙古高速公路养护有限责任公司呼和浩特 010010

基金项目:

国家重点研发计划项目 2021YFB2601000

国家自然科学基金项目 52178409

内蒙古自治区交通运输科技项目 NJ-2021-17

详细信息

通讯作者:
惠冰（1982—），博士，副教授.研究方向：路面检测与养护管理

中图分类号: U491.2
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- 被引次数: 0
出版历程
- 收稿日期: 2022-03-06
- 网络出版日期: 2023-05-13

A Detection Method for Pavement Cracks Based on an Improved U-Shaped Network

HUI Bing^{1, 2
,},
LI Yuanjian³

1.
School of Highway, Chang'an University, Xi'an 710064, China
2.
Key Laboratory of Intelligent Construction and Maintenance of CAAC, Chang'an University, Xi'an 710064, China
3.
Inner Mongolia Expressway Maintenance Co., Ltd, Hohhot 010010, China

摘要

摘要: 针对传统的裂缝分割算法难以识别狭窄裂缝且分割边缘不精准，从而造成识别精度较低的问题，研究了基于改进U型神经网络（Unet）的路面裂缝检测方法。由于传统Unet特征提取网络是层次较浅的浅层神经网络，难以提取更复杂的裂缝特征信息，故本文以牛津大学视觉几何组网络（VGG16）作为传统Unet的特征提取网络，提高网络的裂缝特征提取能力；为抑制高低阶特征融合时产生的无用特征，本文在模型解码部分添加压缩与激励单元（SE block），构建裂缝注意力单元，使得网络可以关注不同通道下的裂缝特征，建立了基于SE block和VGG16的改进Unet网络（SE-VUnet）。研究采用迁移学习的方法，将在ImageNet上预训练好的VGG16网络权重迁移到裂缝检测中。通过挑选Crack500数据集，并使用摄像头采集图片构建1 600张路面裂缝数据集，再次训练SE-VUnet模型，获得裂缝区域分割结果。以查准率（precision）与查全率（recall）的加权调和平均值F1和雅卡尔（Jaccard）相似系数作为量化评价指标。将SE-VUnet分别与Unet、SOLO v2、Mask R-CNN以及Deeplabv3+进行分割效果和实时性对比。研究结果表明：SE-VUnet模型的综合F1和雅卡尔系数分别为0.840 3和0.722 1，相比于Unet分别高出了1.04%和1.51%，且均高于其他3种对比模型；SE-VUnet的单帧图片预测时间为89 ms，在分割效果提升明显的情况下仅比Unet慢5 ms，优于其他模型。
- 信息工程 /
- 裂缝检测 /
- U型神经网络 /
- 深度学习 /
- 语义分割
Abstract: Due to the traditional crack segmentation algorithm is difficult to identify narrow cracks and the segmentation edge is not accurate. This paper proposes a pavement crack detection method based on improved U-Shaped Network (Unet) to increase detection accuracy. Since traditional Unet is a type of"shallow"neural network, it is not good for extracting complex crack features. The Oxford University Visual Geometry Group Network (VGG16) is therefore used for feature extraction, in order to improve the accuracy of crack feature extraction. In addition, the fusion of high- and low-order features generate several useless features. The compression and excitation unit (SE block) is added to the decoding part of the model to develop a crack attention unit which allows the network to focus on the crack features under different channels. Moreover, an improved Unet is proposed by combining SE block with VGG16 (SE-VUnet). In addition, a transfer learning method is used to transfer the pre-trained VGG16 network weight on ImageNet for crack detection. By selecting the Crack500 data set and using the camera to collect images to develop1600 pavement crack data sets, the SE-VUnet model is trained again to obtain the crack segmentation results. The weighted harmonic mean F1 of Precision and Recall and Jaccard similarity coefficient are used as quantitative evaluation indicators. The segmentation effect and real-time performance of SE-VUnet are compared with Unet and three other representative models. Study results show that the comprehensive F1 and the Jaccard coefficient of SE-VUnet model is 0.840 3 and 0.722 1, which is 1.04% and 1.51% higher than Unet respectively, as well as other three comparison models. The time for the SE-VUnet to screen a single-frame image is 89 ms, which is only 5ms slower than the Unet but with a significant improvement over the crack segmentation and detection process.
- information engineering /
- crack detection /
- Unet /
- deep learning /
- semantic segmentation

HTML全文

图 1 整体流程图

Figure 1. Overall flow chart

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图 2 网络模型结构

Figure 2. Structures of network models

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图 3 SE-VUnet模型

Figure 3. The SE-VUnet model

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图 4 Unet编码结构与本文特征提取网络

Figure 4. Unet encoding structure and feature extraction network of this paper

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图 5 SE block结构

Figure 5. SE block

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图 6 Unet解码结构改进

Figure 6. Improvement of Unet decoding structure

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图 7 传统机器学习和迁移学习对比

Figure 7. Comparison of traditional machine learning and transfer learning

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图 8 图像采集平台

Figure 8. Image acquisition platform

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图 9 路面裂缝图像示例

Figure 9. Pavement crack image

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图 10 SE-VUnet与Unet的F1和雅卡尔系数

Figure 10. F1 and Jaccard coefficients of SE-VUnet and Unet

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图 11 狭长裂缝分割结果

Figure 11. Long and narrow crack segmentation result

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图 12 宽裂缝预测结果

Figure 12. Non-narrow crack segmentation result

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图 13 路面裂缝在多个模型下的测试结果

Figure 13. Test results of pavement cracks under multiple models

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图 14 多个模型在裂缝宽度较窄和对比度较低情况下的分割结果

Figure 14. Segmentation results of multiple models in the case of narrow crack width and low contrast

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表 1 试验数据

Table 1. Experimental data

训练验证集	数量/张	测试集	数量/张
狭长裂缝	498	狭长裂缝	91
宽裂缝	852	宽裂缝	159

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表 2 不同模型的推理时间

Table 2. Inference time of different models

模型	推理时间/s
SE-VUnet	0.089
Unet	0.084
SOLO v2	0.130
Mask R-CNN	0.162
Deeplabv3+	0.093
文献[10]	0.360

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表 3 不同模型的F1和Jaccard指标

Table 3. F1 and Jaccard coefficients of different models

模型	Precision	Recall	F1	Jaccard
SE-VUnet	0.803 1	0.881 2	0.840 3	0.722 1
Unet	0.805 3	0.856 4	0.829 9	0.707 0
SOLO v2	0.835 3	0.813 9	0.824 5	0.702 1
Mask R-CNN	0.633 2	0.733 1	0.679 5	0.511 7
Deeplabv3+	0.863 4	0.533 3	0.659 4	0.501 0

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