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.

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 Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1651 ◽  
Author(s):  
Suk-Ju Hong ◽  
Yunhyeok Han ◽  
Sang-Yeon Kim ◽  
Ah-Yeong Lee ◽  
Ghiseok Kim
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Migratory Bird ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Detection Methods ◽  
Single Shot ◽  
Convolutional Network ◽  
Aerial Vehicle ◽  
Bird Detection

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

Structural crack detection using deep learning–based fully convolutional networks

2019 ◽  
Vol 22 (16) ◽  
pp. 3412-3419 ◽  
Author(s):  
Xiao-Wei Ye ◽  
Tao Jin ◽  
Peng-Yu Chen
Keyword(s):  
Edge Detection ◽  
Structural Damage ◽  
Crack Detection ◽  
Training Data ◽  
Detection Methods ◽  
Crack Identification ◽  
Potential Threat ◽  
Online Data ◽  
Convolutional Network ◽  
Data Set

Cracks are a potential threat to the safety and endurance of civil infrastructures, and therefore, careful and regular structural crack inspection is needed during their long-term service periods. Many image-processing approaches have been developed for structural crack detection. However, like traditional edge detection algorithms, these methods are easily disturbed by the environmental effect. Convolutional neural networks are newly developed methods and have excellent performances in the image-classification tasks. This study proposes a fully convolutional network called Ci-Net for structural crack identification. Pixel-level labeled image training data are obtained from the online data set. Four indices are adopted to evaluate the performance of the trained Ci-Net. Crack images from an indoor concrete beam test are adopted for validation of its structural crack recognition capacity. The recognition results are also compared with those obtained by the edge detection methods. It indicates that Ci-Net exhibits a better performance over the edge detection methods in structural damage detection.

scholarly journals Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently

2019 ◽  
Vol 33 ◽  
pp. 4344-4351
Author(s):  
Dan Liu ◽  
Dawei Du ◽  
Libo Zhang ◽  
Tiejian Luo ◽  
Yanjun Wu ◽  
...  
Keyword(s):  
Detection Methods ◽  
Feature Maps ◽  
Hand Detection ◽  
Scale Invariant ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Extraction Region ◽  
Region Detection ◽  
Weighted Fusion ◽  
Intermediate Layers

Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i.e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection. In this paper, we propose a new Scale Invariant Fully Convolutional Network (SIFCN) trained in an end-to-end fashion to detect hands efficiently. Specifically, we merge the feature maps from high to low layers in an iterative way, which handles different scales of hands better with less time overhead comparing to concatenating them simply. Moreover, we develop the Complementary Weighted Fusion (CWF) block to make full use of the distinctive features among multiple layers to achieve scale invariance. To deal with rotated hand detection, we present the rotation map to get rid of complex rotation and derotation layers. Besides, we design the multi-scale loss scheme to accelerate the training process significantly by adding supervision to the intermediate layers of the network. Compared with the state-of-the-art methods, our algorithm shows comparable accuracy and runs a 4.23 times faster speed on the VIVA dataset and achieves better average precision on Oxford hand detection dataset at a speed of 62.5 fps.

scholarly journals A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0196302 ◽  
Author(s):  
Huasheng Huang ◽  
Jizhong Deng ◽  
Yubin Lan ◽  
Aqing Yang ◽  
Xiaoling Deng ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Weed Mapping ◽  
Aerial Vehicle

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.

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