Detection of Crack Growth in Asphalt Pavement Through Use of Infrared Imaging

2017 ◽  
Vol 2645 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Yuchuan Du ◽  
Xiaoming Zhang ◽  
Feng Li ◽  
Lijun Sun
Keyword(s):  
Crack Growth ◽  
Temperature Difference ◽  
Crack Detection ◽  
Infrared Imaging ◽  
Asphalt Pavement ◽  
Atmospheric Temperature ◽  
Support Vector ◽  
Road Maintenance ◽  
Pavement Maintenance ◽  
Detection Model

The degree of crack growth in asphalt pavement is an important decision-making factor in road maintenance management. Automatic crack detection is based mainly on digital images; this factor makes effective detection of the degree of crack growth difficult. Infrared thermography was used, and a detection method for the degree of crack growth on the basis of infrared imaging was proposed. Infrared images included gray-level information on cracks and temperature information; the latter provided one additional dimension of information over ordinary images. Temperature information was used to detect the degree of crack growth. Atmospheric temperature was found to be the main factor that affected the temperature difference between a crack and the road surface. This temperature difference varied significantly for different extents of crack growth, and therefore this difference can be used to detect the degree of crack growth. Two classification functions that divided the degree of crack growth into three grades were obtained by classifying data through the use of a support vector machine. A suitable environmental condition for using the detection model was proposed. The experimental results showed that the average model error was 15.4%, which indicated a good application prospect and an improvement in economic benefit for pavement maintenance.

scholarly journals Pavement Crack Detection and Segmentation Method Based on Improved Deep Learning Fusion Model

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Xiaoran Feng ◽  
Liyang Xiao ◽  
Wei Li ◽  
Lili Pei ◽  
Zhaoyun Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Geometric Parameter ◽  
Crack Detection ◽  
Crack Identification ◽  
Single Shot ◽  
Road Maintenance ◽  
Fusion Model ◽  
Detection Model ◽  
Crack Classification

Pavement damage is the main factor affecting road performance. Pavement cracking, a common type of road damage, is a key challenge in road maintenance. In order to achieve an accurate crack classification, segmentation, and geometric parameter calculation, this paper proposes a method based on a deep convolutional neural network fusion model for pavement crack identification, which combines the advantages of the multitarget single-shot multibox detector (SSD) convolutional neural network model and the U-Net model. First, the crack classification and detection model is applied to classify the cracks and obtain the detection confidence. Next, the crack segmentation network is applied to accurately segment the pavement cracks. By improving the feature extraction structure and optimizing the hyperparameters of the model, pavement crack classification and segmentation accuracy were improved. Finally, the length and width (for linear cracks) and the area (for alligator cracks) are calculated according to the segmentation results. Test results show that the recognition accuracy of the pavement crack identification method for transverse, longitudinal, and alligator cracks is 86.8%, 87.6%, and 85.5%, respectively. It is demonstrated that the proposed method can provide the category information for pavement cracks as well as the accurate positioning and geometric parameter information, which can be used directly for evaluating the pavement condition.

Automatic Identification of Crack in Ultrasonic Infrared Imaging

2012 ◽  
Vol 605-607 ◽  
pp. 1001-1006
Author(s):  
Fu Zhou Feng ◽  
Chao Sheng Zhang ◽  
Qing Xu Min ◽  
Peng Cheng Jiang
Keyword(s):  
Crack Detection ◽  
Infrared Imaging ◽  
Impact Damage ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Automatic Identification ◽  
Identification Algorithm ◽  
Metal Fatigue ◽  
Shape Characters ◽  
Internal Defect Detection

Ultrasonic Infrared Imaging is a novel NDE technique, which performs well on material internal defect detection, such as metal fatigue crack, composite material impact damage and adhesion and so on. Traditional defect identification often depends on eyes and professional experience, which can’t give a clear conclusion of defect information. The identification algorithm based on time sequence images is low-level. Therefore, taking the crack detection in Ultrasonic IR Imaging as an example, after contrastive analysis of shape characters and gray distribution between crack region and normal region, characteristic parameters for different regions was creatively extracted in this paper. An automatic recognition algorithm based on Weighted Support Vector Machines is put forward for crack recognition. Subsequently, the correctness of the algorithm was validated by experiments.

scholarly journals Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

2021 ◽  
Vol 13 (15) ◽  
pp. 3024
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Linyi Liu ◽  
Anting Guo
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Support Vector ◽  
Detection Accuracy ◽  
Feature Combination ◽  
Field Scale ◽  
Head Blight ◽  
Detection Model

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

scholarly journals CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1402
Author(s):  
Taehee Lee ◽  
Yeohwan Yoon ◽  
Chanjun Chun ◽  
Seungki Ryu
Keyword(s):  
Surface Crack ◽  
Autonomous Vehicles ◽  
Crack Detection ◽  
Model Performance ◽  
Semantic Segmentation ◽  
Road Surface ◽  
Image Brightness ◽  
The Road ◽  
Detection Model ◽  
Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

A Review of Crack Detection In-Line Inspection Case Studies

2010 ◽  
Author(s):  
Neil Bates ◽  
David Lee ◽  
Clifford Maier
Keyword(s):  
Crack Growth ◽  
Case Studies ◽  
Crack Detection ◽  
Growth Parameters ◽  
Probability Distributions ◽  
Low Frequency ◽  
Probability Of Failure ◽  
Probability Of Detection ◽  
Inspection Data

This paper describes case studies involving crack detection in-line inspections and fitness for service assessments that were performed based on the inspection data. The assessments were used to evaluate the immediate integrity of the pipeline based on the reported features and the long-term integrity of the pipeline based on excavation data and probabilistic SCC and fatigue crack growth simulations. Two different case studies are analyzed, which illustrate how the data from an ultrasonic crack tool inspection was used to assess threats such as low frequency electrical resistance weld seam defects and stress corrosion cracking. Specific issues, such as probability of detection/identification and the length/depth accuracy of the tool, were evaluated to determine the suitability of the tool to accurately classify and size different types of defects. The long term assessment is based on the Monte Carlo method [1], where the material properties, pipeline details, crack growth parameters, and feature dimensions are randomly selected from certain specified probability distributions to determine the probability of failure versus time for the pipeline segment. The distributions of unreported crack-related features from the excavation program are used to distribute unreported features along the pipeline. Simulated crack growth by fatigue, SCC, or a combination of the two is performed until failure by either leak or rupture is predicted. The probability of failure calculation is performed through a number of crack growth simulations for each of the reported and unreported features and tallying their respective remaining lives. The results of the probabilistic analysis were used to determine the most effective and economical means of remediation by identifying areas or crack mechanisms that contribute most to the probability of failure.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

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