Crack Detection of Concrete Pavement With Cross-Entropy Loss Function and Improved VGG16 Network Model

Qu, Zhong; Mei, Jing; Liu, Ling; Dong-yang, Zhou

doi:10.1109/access.2020.2981561

Cited by 128 publications

(60 citation statements)

References 40 publications

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“…ResNet is currently the most widely used improved CNN network and was proposed in ILSVRC-2015 [47]. The researchers on this project deepened the depth of the network to improve the expression and learning ability of the model in the early stage, from the classic CNN network structure AlexNet (7 layers) to VGGNet-16 and VGGNet-19 [73,74]. However, as the depth of the network deepens, a serious gradient vanish appears, and this problem slows the convergence speed and accuracy of the network.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China

Liu¹,

Chen²,

Niu³

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Landslide detection mapping (LDM) is the basis of the field of landslide disaster prevention; however, it has faced certain difficulties. The Three Gorges Reservoir area of the Yangtze River has been one of the most intensively evaluated areas for landslide prevention in the world, due to the high frequency of landslide disasters here. In this study, we constructed an accurate LDM model based on convolutional neural networks (CNNs), residual neural networks (ResNets), and dense convolutional neural networks (DenseNets) that considers 'ZY-3' high spatial resolution (HSR) data and conditioning factors (CFs). In this work, nineteen factors based on remote sensing (RS) images, topographical and geological data associated with historical landslide locations were randomly divided into training (70% of total) and testing (30%) datasets. The experimental results show that the accuracy (ACC) of these three LDM models is above 0.95, indicating that the deep neural networks (DNNs) aimed at landslide detection performed well. Furthermore, DenseNet with RS images and CFs can accurately detect landslides. Specifically, DenseNet with RS images and CFs outperforms the other five models by considering the accuracy evaluation indexes, which exhibited Kappa coefficient improvements of 0.01-0.04 and ACC improvements of 0.02%-0.3%. Among all the factors, elevation factor has a high importance of 0.727, which is the most important factors found in this landslide model construction experiment.

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China

Liu¹,

Chen²,

Niu³

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…At the same time, DCNN proved to be a much better technique that detected more fine cracks with 86% accuracy. In [12] Authors changed the VGG16 model by integrating the simple two-layer VGG16 model with the Inception Module. The use of two different networks, i.e.…”

Section: A Cnn Deep Learning Model Familymentioning

confidence: 99%

Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model

2021

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Concrete is one of the primary and most commonly used materials for the construction of buildings, roads, bridges, and dams. But it may lose its strength with age, moisture content, or due to other factors. As a result, little damage (crack and spall) to these structures may lead to sudden collapse or breakage, which in turn can decimate many lives with economic losses. Hence, to ensure their strength with the proper load, accurate screening of concrete surface damage is necessary for maintenance engineers to understand and evaluate the severity of the damage. Few preceding vision-based studies have proposed object detection and semantic segmentation approaches to carry out damage detection; however, the developed models could not segment different objects of the same class. The semantic segmentation approach separates regions that contain only objects of the same class. Concrete surface damage, on the other hand, may contain multiple objects, such as the Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall, that needs to be segmented separately so that specific measures can be taken. This study utilizes Mask Region-Based Convolutional Neural Network (Mask R-CNN) to manage concrete damage images and employ it to detect and segment defects in civil infrastructure with multiple objects. As we understand, this is the first research involving a Structural Health Monitoring (SHM) application for damage detection using instance segmentation. Our proposed model has been trained and tested on a dataset containing 800 images (480*480 pixels) of different types of crack and spall are collected from distinct structures of CSIR-CEERI, Pilani, campus. To test and validate the generosity, an additional 96 damage images are downloaded using Google. The pre-processed and annotated images are used to train and validate the Mask R-CNN Classifier. Our experimental results show that damage can be classified efficiently with 95.13% accuracy on a custom dataset and 96.87% on randomly picked images.

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“…The proposed models attained successful experimental results with accuracy of 97.82%, 99.11%, 99.32%, and 98.89%, respectively. In [ 43 ], an improved version of the VGG16 network for the detection of crack is presented, and the authors prepared their dataset, named CCD1500, for training the model, whereas the CFD, DeepCrack, and CrackTree200 datasets are used as test data. The experimental results indicate that the proposed model gained successful detection results with a recall of 90.30% for CFD, 96.60% for DeepCrack, and 89.10% for the CrackTree200 dataset.…”

Section: Introductionmentioning

confidence: 99%

Edge AI‐Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

Bibi

Saeed

Zeb

et al. 2021

Computational Intelligence and Neuroscience

142

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Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

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Crack Detection of Concrete Pavement With Cross-Entropy Loss Function and Improved VGG16 Network Model

Cited by 128 publications

References 40 publications

Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China

Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China

Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model

Edge AI‐Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

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