Depth-stage dependent and hyperparameter-adaptive lightweight convolutional neural network-based model for rapid road crack detection

Patent metadata

Field	Value
Publication number	US-2024233371-A1
Application number	US-202318540915-A
Country	US
Kind code	A1
Filing date	Dec 15, 2023
Priority date	Jan 5, 2023
Publication date	Jul 11, 2024
Grant date	—

Abstract

A depth-stage dependent and hyperparameter-adaptive lightweight CNN-based model, named Faster R-Stair, which relates to the field of concrete crack detection technology. The structure of the backbone in this model is depth-stage dependent, which includes suitable structures in different depths. The backbone is also hyperparameter-adaptive. The basic components in different depths of the backbone have variations according to the adjustment of some hyperparameters. The proposed model in this embodiment has the advantages of high convergence speed in training, fast detection speed and high accuracy when used in crack detection.

First claim

What is claimed is:

1 . A method for fast detecting road cracks in images using a depth-stage dependent and hyperparameter-adaptive lightweight CNN-based model, comprising the following steps:
the original images of road surface are collected and a dataset is established, including a training set and a validation set; the images from the dataset are inputted into the backbone (backbone-Stair) to obtain feature maps, the backbone is depth-stage dependent, which includes suitable structures in different depths: convolutional layer, stair1, a convolution block attention module (CBAM), stair2, another CBAM, and stair3, the basic components in stair1 and stair2 have some variations according to the adjustment of some hyperparameters; stair1 has two variations as follows: when the expansion factor is 1, the input feature maps go through an inverted residual structure with convolutions; when the expansion factor is not 1, the input feature maps go through a convolutional operation; stair2 has two variations as follows: when the kernel stride is 1, the channels of the input feature maps are split into two equal parts using the split operation, one part goes through an inverted residual structure with depth-wise separable convolutions, while the other part remains unchanged, after that, the two sets of channels are concatenated and then subjected to the shuffle operation; when the kernel stride is 2, the channels of the input feature maps are replicated into three copies, one copy goes through an inverted residual structure, another copy goes through a depth-wise separable convolution followed by dimension reduction, the last copy goes through a max pooling operation followed by dimension reduction, finally, the three sets of dimension-reduced channels are concatenated and then subjected to the shuffle operation; stair3 consists of a residual structure consisting of depth separable convolution and efficient channel attention (ECA); and the feature maps obtained from the backbone are inputted to a region proposal network (RPN) to generate proposals, the proposals are projected onto the feature maps outputted by backbone to obtain corresponding feature matrices, the feature matrix is passed through a region of interest (ROI) head to output predicted bounding boxes of the road cracks in the feature maps, the predicted bounding boxes of the road cracks in the feature maps are mapped back to the original image using post-processing to obtain the positions and types of road cracks in the original image.

2 . The method according to claim 1 , wherein a feature of the method for fast detecting road cracks using the Faster R-Stair model is that the input feature map passed through the batch normalization layer (BN) is normalized using the following formula:

μ
ℬ

=

1
m

⁢

∑

i
=
1

m

x
i

σ
ℬ
2

=

1
m

⁢

∑

i
=
1

m

(

x
i

-

μ
ℬ

)

2

x
ˆ

i

=

x
i

-

μ
ℬ

σ
ℬ
2

+
ϵ

y
i

←

γ
⁢

x
ˆ

i

+
β

where in the formula, x i represents the input feature map to batch normalization, y i represents the output feature map after Batch normalization, m represents the number of feature maps input to this layer, and γ and β are variables that vary with the gradient updates of the network.

3 . The method according to claim 1 , wherein a feature of the method for fast detecting road cracks using the Faster R-Stair model is that the data passes through a ReLU6 (RE) activation function in each layer and is subjected to non-linear processing using the following formula:
ƒ( x i )=min(max( x i ,0),6)
where x i is the input data to the ReLU6 activation function, and ƒ(x i ) denotes the output data after the non-linear processing.

4 . The method according to claim 1 , wherein a feature of the method for fast detecting road cracks using the Faster R-Stair model is that the data passes through a Hardswish (HS) activation function in each layer and is subjected to non-linear processing using the following formula:

f
⁡
(
x
)

=

{

Assignees

• Univ Hohai
• Univ Jiangxi Sci & Technology
• Jsti Group Co Ltd

Inventors

• Cao Maosen
• Fu Ronghua
• Deng Tongfa
• Zhang Yufeng
• Qian Xiangdong
• Sumarac Dragoslav
• Li Shuai
• Zhu Kai

Classifications

• G06F18/00

  Pattern recognition · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V20/182 Primary

  Network patterns, e.g. roads or rivers · CPC title

• G06N3/0464 Primary

  Convolutional networks [CNN, ConvNet] · CPC title