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 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 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 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 Instance Segmentation of Road Pavement Cracks

2019 ◽  
Author(s):  
Ирина Канаева ◽  
Irina Kanaeva ◽  
Юлия Иванова ◽  
Yuliya Ivanova
Keyword(s):  
Damage Detection ◽  
Crack Detection ◽  
Current Problem ◽  
Pixel Intensity ◽  
Road Pavement ◽  
Condition Evaluation ◽  
Road Condition ◽  
Complex Crack ◽  
Pavement Crack Detection ◽  
Instance Segmentation

Robust automatic pavement crack detection is critical to automated road condition evaluation. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. This study makes literature review of road damage detection issues. The paper considers the existing datasets for detection and segmentation distress of road and asphalt pavement. A CNN for pavement cracks instance segmentation has been developed with the use of images from the driver's seat view. A method for generating a synthetic dataset is also presented, and effectiveness of its applicability to the current problem is evaluated. The relevance of the study is emphasized by research on pixel-level automatic damage detection remains a challenging problem, due to heterogeneous pixel intensity, complex crack topology, poor illumination condition, and noisy texture background.

Electromigration in Lead – Free Solder: A Power IC Perspective

2013 ◽  
Vol 2013 (1) ◽  
pp. 000753-000757
Author(s):  
Thomas A. Wassick
Keyword(s):  
Power Electronics ◽  
High Performance ◽  
Lead Free ◽  
Lead Free Solder ◽  
Key Factors ◽  
Computing Systems ◽  
Solder Interconnects ◽  
Product Lifetime ◽  
Free Solder ◽  
High Level

Over the past few years, lead - free solder interconnects have been significantly incorporated into electronic products, and are increasingly found in high performance computing systems and in their associated power electronics. As power and current levels increase within these products, the overall reliability of a lead-free solder based system can be impacted by an increasing risk of finding electromigration (EM) degradation during the product lifetime, especially if the product is operating at higher temperatures and with very high current densities. This paper provides a high-level technical overview of lead-free electromigration and describes the key factors and issues that can influence the EM performance of lead-free interconnects, especially in the environments in which power electronics are typically found.

scholarly journals A CAPSNETS APPROACH TO PAVEMENT CRACK DETECTION USING MOBILE LASER SCANNNING POINT CLOUDS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 39-44
Author(s):  
W. Zhu ◽  
W. Tan ◽  
L. Ma ◽  
D. Zhang ◽  
J. Li ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Traffic Accidents ◽  
Crack Detection ◽  
Point Clouds ◽  
Huge Amount ◽  
Processing Efficiency ◽  
The Road ◽  
Dense Point ◽  
Intensity Images ◽  
Pavement Crack Detection

Abstract. Routine pavement inspection is crucial to keep roads safe and reduce traffic accidents. However, traditional practices in pavement inspection are labour-intensive and time-consuming. Mobile laser scanning (MLS) has proven a rapid way for collecting a large number of highly dense point clouds covering roadway surfaces. Handling a huge amount of unstructured point clouds is still a very challenging task. In this paper, we propose an effective approach for pavement crack detection using MLS point clouds. Road surface points are first converted into intensity images to improve processing efficiency. Then, a Capsule Neural Network (CapsNet) is developed to classify the road points for pavement crack detection. Quantitative evaluation results showed that our method achieved the recall, precision, and F1-score of 95.3%, 81.1%, and 88.2% in the testing scene, respectively, which demonstrated the proposed CapsNet framework can accurately and robustly detect pavement cracks in complex urban road environments.

Sign in / Sign up

Export Citation Format

Share Document