ARF-Crack: rotation invariant deep fully convolutional network for pixel-level crack detection

2020 ◽  
Vol 31 (6) ◽  
Author(s):  
Fu-Chen Chen ◽  
Mohammad R. Jahanshahi
Keyword(s):  
Crack Detection ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Fully Convolutional Network

Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network

2018 ◽  
Vol 33 (12) ◽  
pp. 1090-1109 ◽  
Author(s):  
Xincong Yang ◽  
Heng Li ◽  
Yantao Yu ◽  
Xiaochun Luo ◽  
Ting Huang ◽  
...  
Keyword(s):  
Crack Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2069 ◽  
Author(s):  
Chuncheng Feng ◽  
Hua Zhang ◽  
Haoran Wang ◽  
Shuang Wang ◽  
Yonglong Li
Keyword(s):  
Crack Detection ◽  
Health Assessment ◽  
Detection Methods ◽  
Convolutional Network ◽  
Test Dataset ◽  
Fully Convolutional Network ◽  
Aerial Vehicle ◽  
Deep Convolution Network ◽  
Hydropower Stations ◽  
F Measure

Crack detection on dam surfaces is an important task for safe inspection of hydropower stations. More and more object detection methods based on deep learning are being applied to crack detection. However, most of the methods can only achieve the classification and rough location of cracks. Pixel-level crack detection can provide more intuitive and accurate detection results for dam health assessment. To realize pixel-level crack detection, a method of crack detection on dam surface (CDDS) using deep convolution network is proposed. First, we use an unmanned aerial vehicle (UAV) to collect dam surface images along a predetermined trajectory. Second, raw images are cropped. Then crack regions are manually labelled on cropped images to create the crack dataset, and the architecture of CDDS network is designed. Finally, the CDDS network is trained, validated and tested using the crack dataset. To validate the performance of the CDDS network, the predicted results are compared with ResNet152-based, SegNet, UNet and fully convolutional network (FCN). In terms of crack segmentation, the recall, precision, F-measure and IoU are 80.45%, 80.31%, 79.16%, and 66.76%. The results on test dataset show that the CDDS network has better performance for crack detection of dam surfaces.

scholarly journals A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Meng Meng ◽  
Kun Zhu ◽  
Keqin Chen ◽  
Hang Qu
Keyword(s):  
Large Scale ◽  
Crack Detection ◽  
Damage Identification ◽  
Low Cost ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Concrete Crack ◽  
The Common ◽  
Underwater Structures

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

scholarly journals SACN: A Novel Rotating Face Detector Based on Architecture Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 558
Author(s):  
Anping Song ◽  
Xiaokang Xu ◽  
Xinyi Zhai
Keyword(s):  
Face Detection ◽  
Human Face ◽  
Angle Error ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Data Set ◽  
Practical Applications ◽  
Model Size ◽  
Average Angle ◽  
Face Detector

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.

scholarly journals MaskNet: A Fully-Convolutional Network to Estimate Inlier Points

2020 ◽  
Author(s):  
Vinit Sarode ◽  
Animesh Dhagat ◽  
Rangaprasad Arun Srivatsan ◽  
Nicolas Zevallos ◽  
Simon Lucey ◽  
...  
Keyword(s):  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals Parallel Fully Convolutional Network for Semantic Segmentation

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 673-682
Author(s):  
Jian Ji ◽  
Xiaocong Lu ◽  
Mai Luo ◽  
Minghui Yin ◽  
Qiguang Miao ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network
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