Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Song, Weidong; Jia, Guohui; Zhu, Hong; Di, Jia; Gao, Lin

doi:10.1155/2020/6412562

Cited by 93 publications

(42 citation statements)

References 38 publications

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“…Besides the datasets mentioned, several researchers have collected specific datasets to test algorithms (ex. Crackdataset [2]). Those datasets were reserved for studies.…”

Section: ) Vision Datasetsmentioning

confidence: 99%

Automated Pavement Distress Detection, Classification and Measurement: A Review

Benmhahe¹,

Chentoufi²

2021

IJACSA

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Road surface distress is an unavoidable situation due to age, vehicles overloading, temperature changes, etc. In the beginning, pavement maintenance actions took only place after having too much pavement damage, which leads to costly corrective actions. Therefore, scheduled road surface inspections can extend service life while guaranteeing users security and comfort. Traditional manual and visual inspections don't meet the nowadays criteria, in addition to a relatively high time volume consumption. Smart City pavement management preventive approach requires accurate and scalable data to deduce significant indicators and plan efficient maintenance programs. However, the quality of data depends on sensors used and conditions during scanning. Many studies focused on different sensors, Machine Learning algorithms and Deep Neural Networks tried to find a sustainable solution. Besides all these studies, pavement distress measurement stills a challenge in Smarts Cities because distress detection is not enough to decide on maintenance actions required. Damages localization, dimensions and future development should be highly detected on real-time. This paper summarizes the state-of-the-art methods and technologies used in recent years in pavement distress detection, classification and measurement. The aim is to evaluate current methods and highlight their limitations, to lay out the blueprint for future researches. PMS (Pavement Management System) in Smarts Cities requires an automated pavement distress monitoring and maintenance with high accuracy for large road networks.

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“…Besides the datasets mentioned, several researchers have collected specific datasets to test algorithms (ex. Crackdataset [2]). Those datasets were reserved for studies.…”

Section: ) Vision Datasetsmentioning

confidence: 99%

Automated Pavement Distress Detection, Classification and Measurement: A Review

Benmhahe¹,

Chentoufi²

2021

IJACSA

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“…e reason is that the assessment process is dependent of subjective judgment of human technicians [2,3]. erefore, project owners are increasingly seeking for fast, effective, and consistent tools to better structure condition assessment [19][20][21][22][23][24]. e assessment outcomes can also enhance communication between various stakeholders regarding the condition of the buildings.…”

Section: Research Backgroundmentioning

confidence: 99%

A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History‐Based Adaptive Differential Evolution Optimized Support Vector Machine

Hoang

Nguyen

2020

Advances in Civil Engineering

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To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected. To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. Image texture computations employ the Gabor filter and gray-level run lengths to characterize the condition of a concrete surface. Based on features of image texture, Support Vector Machines (SVM) establish a decision boundary that separates collected image samples into two categories of no surface void (negative class) and surface void (positive class). Furthermore, to assist the SVM model training phase, the state-of-the-art history-based adaptive differential evolution with linear population size reduction (L-SHADE) is utilized. The hybrid intelligence approach, named as L-SHADE-SVM-SVD, has been developed and complied in Visual C#.NET framework. Experiments with 1000 image samples show that the L-SHADE-SVM-SVD can obtain a high prediction accuracy of roughly 93%. Therefore, the newly developed model can be a promising alternative for construction inspectors in concrete quality assessment.

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“…Chen et al combined a convolutional neural network with naive Bayes to detect cracks in nuclear inspection videos [ 38 ], where the false positives are subtly removed by the use of the naive Bayes method. Song et al introduced a new multiscale extended convolution module, which can learn plentiful deep features and make the acquired features more recognizable in complex backgrounds [ 39 ]. It is worth highlighting that with the development of computer vision techniques, especially deep learning, automatic object detection technology has been greatly improved.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision-Based Bridge Damage Detection Using Deep Convolutional Networks with Expectation Maximum Attention Module

Qiao

Liu

et al. 2021

Sensors

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Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.

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Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Cited by 93 publications

References 38 publications

Automated Pavement Distress Detection, Classification and Measurement: A Review

Automated Pavement Distress Detection, Classification and Measurement: A Review

A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History‐Based Adaptive Differential Evolution Optimized Support Vector Machine

Computer Vision-Based Bridge Damage Detection Using Deep Convolutional Networks with Expectation Maximum Attention Module

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