Gabor滤波融合卷积神经网络的路面裂缝检测方法

陈晓冬; 艾大航; 张佳琛; 蔡怀宇; 崔克让

doi:10.37188/CO.2020-0041

Gabor滤波融合卷积神经网络的路面裂缝检测方法

doi: 10.37188/CO.2020-0041

cstr: 32171.14.CO.2020-0041

1.
天津大学精密仪器与光电子工程学院光电信息技术教育部重点实验室，天津 300072
2.
天津市公路工程设计研究院，天津 300201

基金项目: 天津市交通运输科技发展项目（No. 2019-03）

详细信息

作者简介:
陈晓冬（1975—），男，浙江温州人，博士，教授，博士生导师，1996年、2002年于天津大学分别获得硕士、博士学位，主要从事光电成像与检测技术方向的研究。E-mail：xdchen@tju.edu.cn

艾大航（1996—），男，山西晋中人，硕士研究生，2018年于天津大学获得学士学位，主要从事智能交通图像处理技术的研究。E-mail：aidh@tju.edu.cn

中图分类号: TP391.4
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出版历程
- 收稿日期: 2020-03-16
- 修回日期: 2020-05-07
- 网络出版日期: 2020-10-22
- 刊出日期: 2020-12-01

Gabor filter fusion network for pavement crack detection

1.
Key Laboratory of Photoelectric Information, Ministry of Education, School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
2.
Tianjin Highway Engineering Design and Research Institute, Tianjin 300201, China

Funds: Transportation Technology Development Project of Tianjin (No. 2019-03)

More Information

Corresponding author: aidh@tju.edu.cn

摘要

摘要: 针对神经网络难以从数据量较少、较单一的路面裂缝图像中提取有效特征的局限性，设计了一种融合Gabor滤波器的卷积神经网络模型（Gabor Filter Convolutional Neural Network，GF-CNN）以进行路面裂缝检测。GF-CNN模型首先将路面图像输入小型参数预测网络中，依据输入图像自适应地选取Gabor滤波器组的参数，并根据所预测参数构建滤波器组对初始路面图像进行滤波，得到Gabor纹理特征图。将纹理特征图输入至基于残差网络构建的特征分类网络中提取深层特征，判断其是否包含裂缝。在GAPs路面图像数据集上的测试结果表明，GF-CNN模型的F1分数达到0.7137，优于其他路面图像检测方法。该模型通过融合纹理特征改善CNN特征提取能力，同时降低Gabor滤波器参数敏感性以提高模型泛化能力，对于路面裂缝图像具有良好的适用性。
- 卷积神经网络 /
- Gabor滤波 /
- 裂缝检测 /
- 纹理特征
Abstract: In pavement detection, the small sample of road crack image data makes it difficult for neural networks to extract useful features from images. To solve this problem, this paper proposes a Gabor Filter Convolutional Neural Network (GF-CNN) model. The GF-CNN model first inputs a road surface image into a small parameter prediction network, adaptively selects the parameters of the Gabor filter bank according to the input, and constructs a filter bank according to the predicted parameters, and then filters the initial road surface image to obtain the Gabor texture feature map. The texture feature map is inputted into a feature classification network constructed by the residual network to extract deep features, at the same time, to judge whether a crack exists. Test results on the GAPs pavement image dataset show that the F1 score of the GF-CNN model reaches 0.7137, which is superior to other pavement image detection methods. This model improves the feature extraction ability of CNNs by fusing texture features, and reduces the sensitivity of Gabor filter parameters to improve its ability to make generalizations. It has good applicability to pavement crack image detection.
- convolutional neural network /
- Gabor filter /
- crack detection /
- texture feature

HTML全文

图 1 AlexNet学习的第一层卷积层参数及Gabor滤波器组

Figure 1. Parameters of the first convolutional layer of AlexNet filters and Gabor filters

下载: 全尺寸图片幻灯片

图 2 GF-CNN网络结构图

Figure 2. GF-CNN network structure

下载: 全尺寸图片幻灯片

图 3 特征分类子网络结构

Figure 3. Sub-network structure of feature classification

下载: 全尺寸图片幻灯片

图 4 完整路面图像检测结果

Figure 4. Detection results of the complete road image

下载: 全尺寸图片幻灯片

图 5 路面图像块检测结果

Figure 5. Detection results of the road image blocks

下载: 全尺寸图片幻灯片

表 1 Gabor参数预测网络结构

Table 1. Prediction network structure of Gabor parameter

名称	类型	步长/像素	输出特征图尺寸/pixel	特征图通道数
输入层	—	—	64×64	1
卷积层	Conv 7×7	1	64×64	8
激活层	ReLU	—	64×64	8
最大池化层	2×2	2	32×32	8
卷积层	Conv 3×3	1	32×32	16
激活层	ReLU	—	32×32	16
最大池化层	2×2	2	16×16	16
卷积层	Conv 3×3	1	16×16	32
激活层	ReLU	—	16×16	32
最大池化层	2×2	2	8×8	32
全连接层	—	—	—	96
激活层	Sigmoid	—	—	96

下载: 导出CSV

表 2 裂缝检测模型对比实验结果

Table 2. Comparison of experimental results of crack detection models

	精度	召回率	F1
CrackIT	0.4694	0.5394	0.4882
ASINVOS	0.6113	0.4994	0.5497
ResNet	0.6837	0.5750	0.6246
GF-CNN	0.8003	0.6441	0.7137

下载: 导出CSV

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