scholarly journals Automatic Road Pavement Assessment with Image Processing: Review and Comparison

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Sylvie Chambon ◽  
Jean-Marc Moliard
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
State Of The Art ◽  
Civil Infrastructures ◽  
The Road ◽  
Multi Scale ◽  
Road Pavement ◽  
Optical Images ◽  
Detection Approach ◽  
Pavement Crack Detection

In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

scholarly journals Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Weidong Song ◽  
Guohui Jia ◽  
Hong Zhu ◽  
Di Jia ◽  
Lin Gao
Keyword(s):  
High Performance ◽  
Crack Detection ◽  
Automated Detection ◽  
Key Factors ◽  
The Road ◽  
Intelligent Maintenance ◽  
Road Pavement ◽  
Maintenance Systems ◽  
High Level ◽  
Pavement Crack Detection

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

scholarly journals COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES

2016 ◽  
Vol XLI-B1 ◽  
pp. 689-694 ◽  
Author(s):  
A. Miraliakbari ◽  
S. Sok ◽  
Y. O. Ouma ◽  
M. Hahn
Keyword(s):  
Comparative Evaluation ◽  
Crack Detection ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Segmentation Methods ◽  
Increasing Demand ◽  
Rotational Invariant ◽  
Pavement Crack Detection

With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

scholarly journals COMPARATIVE EVALUATION OF PAVEMENT CRACK DETECTION USING KERNEL-BASED TECHNIQUES IN ASPHALT ROAD SURFACES

2016 ◽  
Vol XLI-B1 ◽  
pp. 689-694
Author(s):  
A. Miraliakbari ◽  
S. Sok ◽  
Y. O. Ouma ◽  
M. Hahn
Keyword(s):  
Comparative Evaluation ◽  
Crack Detection ◽  
Pavement Condition ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Segmentation Methods ◽  
Increasing Demand ◽  
Rotational Invariant ◽  
Pavement Crack Detection

With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and evaluation of the actual road conditions. This is due in part to the resulting large volumes of road pavement data captured through digital surveys, and also to the requirements for rapid data processing and evaluations. In this study, the Canon 5D Mark II RGB camera with a resolution of 21 megapixels is used for the road pavement condition mapping. Even though many imaging and mapping sensors are available, the development of automated pavement distress detection, recognition and extraction systems for pavement condition is still a challenge. In order to detect and extract pavement cracks, a comparative evaluation of kernel-based segmentation methods comprising line filtering (LF), local binary pattern (LBP) and high-pass filtering (HPF) is carried out. While the LF and LBP methods are based on the principle of rotation-invariance for pattern matching, the HPF applies the same principle for filtering, but with a rotational invariant matrix. With respect to the processing speeds, HPF is fastest due to the fact that it is based on a single kernel, as compared to LF and LBP which are based on several kernels. Experiments with 20 sample images which contain linear, block and alligator cracks are carried out. On an average a completeness of distress extraction with values of 81.2%, 76.2% and 81.1% have been found for LF, HPF and LBP respectively.

scholarly journals Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Zhou ◽  
Chengdong Wu ◽  
Dali Chen ◽  
Zhenzhu Wang ◽  
Yugen Yi ◽  
...  
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Dictionary Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Critical Role ◽  
Medical Image Processing ◽  
Unified Framework ◽  
Semantic Relationships ◽  
Detection Approach

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

scholarly journals Cement Pavement Surface Crack Detection Based on Image Processing

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hongwei Lei ◽  
Jianlian Cheng ◽  
Qi Xu
Keyword(s):  
Image Processing ◽  
Laplace Operator ◽  
Image Recognition ◽  
Surface Crack ◽  
Iteration Method ◽  
Crack Detection ◽  
Binary Image ◽  
Threshold Method ◽  
Otsu Method ◽  
Pavement Crack Detection

This article introduces the application of image recognition technology in cement pavement crack detection and put forward to method for determining threshold about grayscale stretching. the algorithm is designed about binarization which has a self-adaptive characteristic. After the image is preprocessed, we apply 2D Wavelet and Laplace operator to process the image. According to the characteristic of pixel of gray image, an algorithm designed on binarization for Binary image. The feasibility of this method can be verified the image processed by comparing with the results of three algorithms: Otsu method, iteration method and fixed threshold method.

scholarly journals Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

scholarly journals Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

2020 ◽  
Vol 12 (18) ◽  
pp. 3084 ◽  
Author(s):  
Mohamed Abdellatif ◽  
Harriet Peel ◽  
Anthony G. Cohn ◽  
Raul Fuentes
Keyword(s):  
Crack Detection ◽  
Early Stage ◽  
Visible Spectrum ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Surface Material ◽  
Pavement Condition ◽  
The Road ◽  
Road Pavement ◽  
Crack Index

Detection of road pavement cracks is important and needed at an early stage to repair the road and extend its lifetime for maintaining city roads. Cracks are hard to detect from images taken with visible spectrum cameras due to noise and ambiguity with background textures besides the lack of distinct features in cracks. Hyperspectral images are sensitive to surface material changes and their potential for road crack detection is explored here. The key observation is that road cracks reveal the interior material that is different from the worn surface material. A novel asphalt crack index is introduced here as an additional clue that is sensitive to the spectra in the range 450–550 nm. The crack index is computed and found to be strongly correlated with the appearance of fresh asphalt cracks. The new index is then used to differentiate cracks from road surfaces. Several experiments have been made, which confirmed that the proposed index is effective for crack detection. The recall-precision analysis showed an increase in the associated F1-score by an average of 21.37% compared to the VIS2 metric in the literature (a metric used to classify pavement condition from hyperspectral data).

scholarly journals Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery

2019 ◽  
Author(s):  
Farzaneh Dadrasjavan ◽  
Nima Zarrinpanjeh ◽  
Azam Ameri
Keyword(s):  
Crack Detection ◽  
The Road ◽  
Remotely Sensed Imagery ◽  
Road Pavement ◽  
Road Surface Monitoring ◽  
Surface Monitoring ◽  
Key Frames ◽  
On The Road ◽  
Edge Based ◽  
Complicated Task

Road surface monitoring more specifically crack detection on the surface of the road pavement is a complicated task which is found vital due to critical nature of roads as elements of transportation infrastructure. Cracks on the road pavement is detectable using remotely sensed imagery or car mounted platforms. UAV’s are also considered as useful tools for acquiring reliable information about the pavement of the road. In This paper, an automatic method for crack detection on the road pavement is proposed using acquired videos from UAV platform. Selecting key frames and generating Ortho-image, violating non road regions in the scene are removed. Then through an edge based approach hypothesis crack elements are extracted. Afterwards, through SVM based classification true cracks are detected. Developing the proposed method, the generated results show 75% accuracy in crack detection while less than 10% of cracks are omitted.

scholarly journals AUTOMATED SHAPE-BASED PAVEMENT CRACK DETECTION APPROACH

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 598-608 ◽  
Author(s):  
Teng Wang ◽  
Kasthurirangan Gopalakrishnan ◽  
Omar Smadi ◽  
Arun K. Somani
Keyword(s):  
Crack Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
False Alarms ◽  
Polynomial Curve ◽  
Detection Approach ◽  
Vector Machines ◽  
Different Types ◽  
Surface Irregularities ◽  
Pavement Crack Detection

Pavements are critical man-made infrastructure systems that undergo repeated traffic and environmental loadings. Consequently, they deteriorate with time and manifest certain distresses. To ensure long-lasting performance and appropriate level of service, they need to be preserved and maintained. Highway agencies routinely employ semiautomated and automated image-based methods for network-level pavement-cracking data collection, and there are different types of pavement-cracking data collected by highway agencies for reporting and management purposes. We design a shape-based crack detection approach for pavement health monitoring, which takes advantage of spatial distribution of potential cracks. To achieve this, we first extract Potential Crack Components (PCrCs) from pavement images. Next, we employ polynomial curve to fit all pixels within these components. Finally, we define a Shape Metric (SM) to distinguish crack blocks from background. We experiment the shape-based crack detection approach on different datasets, and compare detection results with an alternate method that is based on Support Vector Machines (SVM) classifier. Experimental results prove that our approach has the capability to produce higher detections and fewer false alarms. Additional research is needed to improve the robustness and accuracy of the developed approach in the presence of anomalies and other surface irregularities.

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