scholarly journals SCCDNet: A Pixel-Level Crack Segmentation Network

2021 ◽  
Vol 11 (11) ◽  
pp. 5074
Author(s):  
Haotian Li ◽  
Zhuang Yue ◽  
Jingyu Liu ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
Feature Map ◽  
Connected Structure

Cracks are one of the most serious defects that threaten the safety of bridges. In order to detect different forms of cracks in different collection environments quickly and accurately, we proposed a pixel-level crack segmentation network based on convolutional neural networks, which is called the Skip Connected Crack Detection Network (SCCDNet). The network is composed of three parts: the Encoder module with 13 convolutional layers pretrained in the VGG-16 network, the Decoder module with a densely connected structure, and the Skip-Squeeze-and-Excitation (SSE) module which connects the feature map shaving the same resolution in the Encoder and Decoder. We used depthwise separable convolution to improve the accuracy of crack segmentation while reducing the complexity of the model. In this paper, a dataset containing cracks collected in different scenes was established, and SCCDNet was trained and tested on this dataset. Compared with the advanced models, SCCDNet obtained the best crack segmentation performance, while F-score reached 0.7763.

scholarly journals Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

Coatings ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 152 ◽  
Author(s):  
Zhun Fan ◽  
Chong Li ◽  
Ying Chen ◽  
Paola Di Mascio ◽  
Xiaopeng Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
The Other ◽  
Detection Methods ◽  
Deep Convolutional Neural Networks ◽  
Predicted Probability ◽  
Low Efficiency ◽  
The Individual ◽  
Pavement Crack Detection

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.

scholarly journals Structural crack detection using deep convolutional neural networks

2022 ◽  
Vol 133 ◽  
pp. 103989
Author(s):  
Raza Ali ◽  
Joon Huang Chuah ◽  
Mohamad Sofian Abu Talip ◽  
Norrima Mokhtar ◽  
Muhammad Ali Shoaib
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Crack Detection ◽  
Deep Convolutional Neural Networks
Sign in / Sign up

Export Citation Format

Share Document