Objective

2

The highway mileage China has outreached 150 000 kilometers till 2020 guided by Highway Network Planning 2013-2030. Road conditions evaluation has become one critical issue for China highway network further. Road crack detection is one of the key techniques to identify and locate crack objects for traffic safety. However

deep learning based cracked objects detection is challenged to cracked pixels and non-cracked pixels issues of single image. Current attention mechanism is recognized as a deep learning module. It strengthens the consistency of weight-related of crack objects during the training process

and improves the deep learning based crack detection performance. The low accuracy of the typical deep neural network needs to be improved in terms of the crack image detection of more complex background and more interference. Thanks to the road crack dataset of Crack500

our deep learning based road crack detection network is facilitated in the context of dual attention mechanism.

Method

2

To deal with the issues mentioned above

a dual attention mechanism integrated road crack detection network is designed. The ResNet-101 network that used dilated convolution is as the basic feature extraction network of the model. The ResNet network has the following features as below: 1) the number of parameters can be manipulated; 2) our network levels are clarified

and the number of multilayer's feature maps have their output features ability; 3) the network uses fewer pooling layers and redundant downsampling layers to improve transmission efficiency; 4) the network does not use dropout layer but batch normalization(BN) and global average pooling layer to regularize training process for speeding up; 5) when the number of network layers is high

the number of 3×3 convolution layers is reduced

and 1×1 convolution layers are used to control the number of input and output feature maps. The ResNet-101 network contains a total of 4 residual groups

including 3

4

23

and 3 residual blocks

respectively. Therefore

a lightweight attention mechanism is relevant to the end of the residual module for a residual attention module. The lightweight attention mechanism is composed of spatial attention mechanism and channel attention mechanism. The 4 residual groups of ResNet-101 used 3

4

23

and 3 residual attention modules

respectively. It is used to enhance consistent weight relationship of the crack objects to realize replicated features extraction of the higher layer of the crack. Giving a medium feature map

the weights relationship is sequentially inferred along the two dimensions of space and channel. Then

multiplying the original feature map to meet the adequate features. It can be seamlessly integrated into any convolutional neural network (CNN) architecture. It also can be trained end-to-end with the CNN together. Our demonstration introduced a non-local attention mechanism at the end of the ResNet-101 network. We obtained the related weight of the highest layer crack feature and achieved the crack detection result. Similarly

the attention mechanism of non-local computing module is related to spatial attention mechanism and channel attention mechanism. Spatial features are updated by the weighted features aggregation in all spots of the image. The weight is determined in terms of the similarity of the features in the two spaces. The channel attention mechanism also applies a similar self-attention mechanism to learn the relationship between any two channel mappings. It updates each channel through the weighted aggregation of all channels as well. Our coding work is implemented based on the pytorch deep learning framework. We carried out stochastic gradient descent(SGD) optimization with an initial learning rate of 0.000 1. The mean intersection over union (mIoU)

pixel accuracy (PA)

and iteration time are as the evaluation indicators of deep learning models.

Result

2

The effectiveness of this network is verified through 4 categories of comparative experiments. The first category is used to evaluate the detection performance of various combination ways of channel attention mechanism and spatial attention mechanism in the residual attention module. The best interactive way is to integrate channel attention mechanism and spatial attention mechanism in parallel. Compared to the other two interactive ways

the mIoU increases 2.11% and 11.29%each; each PA increases by 2.08% and 0.23%. The second result is used to evaluate the effectiveness of the residual attention module.

the residual attention module added mIoU and PA increase by 2.34% and 3.01% in comparison with non-residual attention module. The third illustration is used to contrast the effect of common convolution and dilated convolution

the mIoU and PA of using dilated convolution increased by 6.65% and 4.18%. The final one is used to evaluate the detection performance of our network and some deep neural networks. Compared to fully convolutional network (FCN)

pyramid scene parsing network (PSPNet)

image cascade network (ICNet)

point-wise spatial attention network (PSANet)

dense atrous spatial pyramid pooling (DenseASPP) FCN

PSPNet

ICNet

our mIoU obtained increases by 7.67%

1.54%

6.51%

7.76%

7.70%

respectively. Our PA results increases by 2.94%

0.42%

3.34%

2.13%

-1.59%

respectively.

Conclusion

2

Our network is combined the ResNet-101 network with dilated convolution and dual attention mechanism. While maintaining the resolution of the feature map and improving the receptive field

this network has its priority to adapt to crack objects with complex background and more interference. Our analyzed results show that our mIoU and PA results have promoted current deep neural networks ability.