Weighted Neighborhood Pixels Segmentation Method for Automated Detection of Cracks on Pavement Surface Images

Sun, Lu; Kamaliardakani, Mojtaba; Zhang, Yongming

doi:10.1061/(asce)cp.1943-5487.0000488

Cited by 47 publications

(26 citation statements)

References 27 publications

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“…Automatic crack detection methods using imageprocessing and machine-learning algorithms are relatively efficient and safe for monitoring the road pavement conditions compared with manual inspections or those involving specialized vehicles (Haas, 1996;Radopoulou & Brilakis, 2016;Sun, Kamaliardakani, & Zhang, 2015;Zalama, Gómez-García-Bermejo, Medina, & Llamas, 2014;Zou, Cao, Li, Mao, & Wang, 2012). Although previous researchers have proposed highly accurate methods for automatic road crack detection, the existing detection methods cannot be used to analyze the images taken by black-box cameras directly owing to the lack of generalization capability of these methods.…”

Section: F I G U R E 1 Examples Of Black-box Imagesmentioning

confidence: 99%

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Bang

Park

Kim

et al. 2019

Computer aided Civil Eng

353

170

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Timely monitoring of pavement cracks is essential for successful maintenance of road infrastructure. Accurate information concerning crack location and severity enables proactive management of the infrastructure. Black‐box cameras, which are becoming increasingly widespread at an affordable price, can be used as efficient road‐image collectors over a wide area. However, the cracks in these images are difficult to detect, because the images containing them often include objects other than roads. Thus, we propose a pixel‐level detection method for identifying road cracks in black‐box images using a deep convolutional encoder–decoder network. The encoder consists of convolutional layers of the residual network for extracting crack features, and the decoder consists of deconvolutional layers for localizing the cracks in an input image. The proposed network was trained on 427 out of 527 images extracted from black‐box videos and tested on the remaining 100 images. Compared with VGG‐16, ResNet‐50, ResNet‐101, ResNet‐200 with transfer learning, and ResNet‐152 without transfer learning, ResNet‐152 with transfer learning exhibited the best performance, achieving recall, precision, and intersection of union of 71.98%, 77.68%, and 59.65%, respectively. The experimental results prove that the proposed method is optimal for detecting cracks in black‐box images at the pixel level.

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Section: F I G U R E 1 Examples Of Black-box Imagesmentioning

confidence: 99%

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Bang

Park

Kim

et al. 2019

Computer aided Civil Eng

353

170

View full text Add to dashboard Cite

show abstract

“…Moreover, according to the National Highway Tra c Safety Administration, 16% of tra c crashes are produced due to roadway environmental factors mainly by poor pavement conditions [2]. Poor road conditions also lead to excessive wear on vehicles and tend to increase the number of delays and crashes which can lead to additional financial losses [3]. Currently, manual inspection is the most common technique for identifying pavement distress road surveys [4].…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Road Crack Mapping Using an Optimized Convolutional Neural Network

et al. 2019

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Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour.

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“…Besides the widely used image thresholding methods, the beamlet transform [ 9 ], predesigned image filtering [ 10 ], the Gabor filter [ 11 ], weighted neighborhood segmentation [ 12 ], wavelet-morphology-based detection [ 13 ], fuzzy Hough transform [ 14 ], steerable matched filtering [ 15 ], probabilistic generative model [ 4 ], and optimized minimal path selection [ 16 ] have been investigated by various scholars. Deep learning approaches [ 17 , 18 ] which automate the feature extraction process have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Hoang

2018

Computational Intelligence and Neuroscience

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To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

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Weighted Neighborhood Pixels Segmentation Method for Automated Detection of Cracks on Pavement Surface Images

Cited by 47 publications

References 27 publications

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Real‐Time Road Crack Mapping Using an Optimized Convolutional Neural Network

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

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