Crack Detection Using Enhanced Hierarchical Convolutional Neural Networks (original) (raw)
Related papers
IEEE Access, 2021
Drone imagery is increasingly used in automated inspection for infrastructure surface defects, especially in hazardous or unreachable environments. In machine vision, the key to crack detection rests with robust and accurate algorithms for image processing. To this end, this paper proposes a deep learning approach using hierarchical convolutional neural networks with feature preservation (HCNNFP) and an intercontrast iterative thresholding algorithm for image binarization. First, a set of branch networks is proposed, wherein the output of previous convolutional blocks is half-sizedly concatenated to the current ones to reduce the obscuration in the down-sampling stage taking into account the overall information loss. Next, to extract the feature map generated from the enhanced HCNN, a binary contrast-based autotuned thresholding (CBAT) approach is developed at the post-processing step, where patterns of interest are clustered within the probability map of the identified features. The proposed technique is then applied to identify surface cracks on the surface of roads, bridges or pavements. An extensive comparison with existing techniques is conducted on various datasets and subject to a number of evaluation criteria including the average F-measure (AFβ) introduced here for dynamic quantification of the performance. Experiments on crack images, including those captured by unmanned aerial vehicles inspecting a monorail bridge. The proposed technique outperforms the existing methods on various tested datasets especially for GAPs dataset with an increase of about 1.4% in terms of AFβ while the mean percentage error drops by 2.2%. Such performance demonstrates the merits of the proposed HCNNFP architecture for surface defect inspection.
Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system
Computer-Aided Civil and Infrastructure Engineering, 2019
Crack information provides important evidence of structural degradation and safety in civil structures. Existing inspection methods are inefficient and difficult to rapidly deploy. A real-time crack inspection method is proposed in this study to address this difficulty. Within this method, a wall-climbing unmanned aerial system (UAS) is developed to acquire detailed crack images without distortion, then a wireless data transmission method is applied to fulfill real-time detection requirements, allowing smartphones to receive real-time video taken from the UAS. Next, an image data set including 1,330 crack images taken by the wall-climbing UAS is established and used for training a deep-learning model. For increasing detection speed, state-of-theart convolutional neural networks (CNNs) are compared and employed to train the crack detector; the selected model is transplanted into an android application so that the detection of cracks can be undertaken on a smartphone in real time. Following this, images with cracks are separated and crack width is calculated using an image processing method. The proposed method is then applied to a building where crack information is acquired and calculated accurately with high efficiency, thus verifying the practicability of the proposed method and system.
Drones
Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (Cy...
Crack Detection using Deep Learning
IRJET, 2023
Concrete cracks are one of the primary signs of a structure that would cause major harm to the entire infrastructure. The conventional method of addressing cracks is manual inspection. We discovered during the survey that there are numerous existing technologies, including approaches based on computer vision and image processing. There are several techniques for locating fractures on different surfaces utilizing manual inputs. Different factors influenced the performance of traditional models, which provided results with variable degrees of accuracy. Because there are many different strategies that can be used to produce a good crack detection output, the performance and efficiency were influenced by the environment. Using a Convolutional Neural Network, we have constructed a deep learning model for fracture classification and segmentation. Convolution, activation, and pooling layers make up the foundation of convolutional neural networks. These layers enhance the performance and generalization of neural networks by extracting picture information, introducing nonlinearity, and reducing feature dimensionality. The observed model's accuracy was between 97 and 98 percent. This model can be used to keep track of the condition of the concrete surfaces of bridges, tunnels, and other public transportation infrastructure.
DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection
IEEE Transactions on Image Processing, 2019
Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which brings great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep con-volutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger-scale feature maps and more holistic representations are made in smaller-scale feature maps. We build DeepCrack net on the encoder-decoder architecture of SegNet, and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves F-Measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.
Surface Crack Detection Using Hierarchal Convolutional Neural Network
2019
Cracks on surface walls may imply that a building possesses problems with its structural integrity. Evaluating these types of defects needs to be accurate to determine the condition of the building. Currently, the evaluation of surface cracks is conducted through visual inspection, resulting in occasions of subjective judgements being made on the classification and severity of the surface crack which poses danger for customers and the environment as it not being analysed objectively. Previous researchers have applied numerous classification methods, but they always stop their research at just being able to classify cracks which would not be fully useful for professionals such as surveyors. We propose building a hybrid web application that can classify the condition of a surface from images using a trained Hierarchal-Convolutional Neural Network (H-CNN) which can also decipher if the image that is being looked is a surface or not. For continuous improvement of the H-CNN’s accuracy, t...
Sustainability
Crack detection on roads is essential nowadays because it has a significant impact on ensuring the safety and reliability of road infrastructure. Thus, it is necessary to create more effective and precise crack detection techniques. A safer road network and a better driving experience for all road users can result from the implementation of the ROAD (Robotics-Assisted Onsite Data Collecting) system for spotting road cracks using deep learning and robots. The suggested solution makes use of a robot vision system’s capabilities to gather high-quality data about the road and incorporates deep learning methods for automatically identifying cracks. Among the tested algorithms, Xception stands out as the most accurate and predictive model, with an accuracy of over 90% during the validation process and a mean square error of only 0.03. In contrast, other deep neural networks, such as DenseNet201, InceptionResNetV2, MobileNetV2, VGG16, and VGG19, result in inferior accuracy and higher losse...
Influence of image noise on crack detection performance of deep convolutional neural networks
ArXiv, 2021
Development of deep learning techniques to analyse image data is an expansive and emerging field. The benefits of tracking, identifying, measuring, and sorting features of interest from image data has endless applications for saving cost, time, and improving safety. Much research has been conducted on classifying cracks from image data using deep convolutional neural networks; however, minimal research has been conducted to study the efficacy of network performance when noisy images are used. This paper will address the problem and is dedicated to investigating the influence of image noise on network accuracy. The methods used incorporate a benchmark image data set, which is purposely deteriorated with two types of noise, followed by treatment with image enhancement pre-processing techniques. These images, including their native counterparts, are then used to train and validate two different networks to study the differences in accuracy and performance. Results from this research re...
A Cost Effective Solution for Road Crack Inspection using Cameras and Deep Neural Networks
arXiv: Computer Vision and Pattern Recognition, 2019
Automatic crack detection on pavement surfaces is an important research field in the scope of developing an intelligent transportation infrastructure system. In this paper, a cost effective solution for road crack inspection by mounting commercial grade sport camera, GoPro, on the rear of the moving vehicle is introduced. Also, a novel method called ConnCrack combining conditional Wasserstein generative adversarial network and connectivity maps is proposed for road crack detection. In this method, a 121-layer densely connected neural network with deconvolution layers for multi-level feature fusion is used as generator, and a 5-layer fully convolutional network is used as discriminator. To overcome the scattered output issue related to deconvolution layers, connectivity maps are introduced to represent the crack information within the proposed ConnCrack. The proposed method is tested on a publicly available dataset as well our collected data. The results show that the proposed method...