Pavement Distress Detection With Picucha Methodology for Area-Scan Cameras and Dark Images

Salini, Reus; Xu, Bugao; Paplauskas, Paulius

doi:10.14311/cej.2017.01.0004

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…Besides this, there are so many other methods through which we can easily do the segmentation process. In the segmentation process, we have to divide the image into different segments through thresholding using different techniques like watershed techniques and iteration thresholding, and so on [41], in the iteration thresholding we use to find the minimum and maximum gray values to find the initial thresholding value, that is, T(i) for the functional variable in the following equation.…”

Section: G(x Y) � T[f(x Y)]mentioning

confidence: 99%

“…e automatic real-time detection systems currently in use focus on low entry rates and soldering difficulties, but still, there is no precise algorithm for identification and classification perfectly [11]. Despite the fact that there are some automatic inspection systems currently in use, systems with surface-scan cameras have low differential distortion problems and the resolution of dynamically acquired images is not satisfactory [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental and Numerical Research of Paved Microcrack Using Histogram Equalization for Detection and Segmentation

Ullah

Sun

Elahi

et al. 2022

Mathematical Problems in Engineering

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The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research.

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Section: G(x Y) � T[f(x Y)]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Research of Paved Microcrack Using Histogram Equalization for Detection and Segmentation

Ullah

Sun

Elahi

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…PICUCHA could perform the analysis without problems and the final distresses assessment was made according to the Lithuanian standards. No images pre-processing was required [17].…”

Section: Figure 1 -A Line-scan Camera-laser Illuminator Device On a Pmentioning

confidence: 99%

Hybrid Human-Artificial Intelligence Approach for Pavement Distress Assessment (Picucha)

Salini¹,

Xu²,

Carvalho³

2017

CEJ

Self Cite

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The pavement surface condition assessment is a critical component for a proper pavement management system as well as for pavement rehabilitation design. A number of devices were developed to automatically record surface distresses in a continuous survey mode, but the software required for automatic distress identification remains a big challenge. In this study, a new method named PICture Unsupervised Classification with Human Analysis (PICUCHA) is proposed to circumvent many of the limitations of existing approaches, based on a combination of human and artificial intelligence. It was designed from scratch to be capable to identify sealed and unsealed cracks, potholes, patches, different types of pavements and others. The self-learning algorithms do not use any distresses predefinition and can process images taken by cameras with different brands, technologies and resolution. This study describes some key aspects of the new method and provides examples in which PICUCHA was tested in real conditions showing accuracy up to 96.9% in image pattern detection and classification.

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“…Road networks are crucial infrastructures that provide transport services, and a catalyst for the economic development of a nation. A large part of the current road infrastructure is approaching the end of its service life, which is placing even more pressure on the budgets in the coming years [1]. Road maintenance involves monitoring road performance and repairing defects such as potholes, pavement cracks and rutting.…”

Section: Introductionmentioning

confidence: 99%

Flexible Pavement Crack’s Severity Identification and Classification using Deep Convolution Neural Network

Ibrahim¹,

Zukri²,

Ismail³

et al. 2021

JMechE

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Effective road maintenance program is vital to ensure traffic safety, serviceability, and prolong the life span of the road. Maintenance will be carried out on pavements when signs of degradation begin to appear and delays may also lead to increased maintenance costs in the future, when more severe changes may be required. In Malaysia, manual visual observation is practiced in the inspection of distressed pavements. Nonetheless, this method of inspection is ineffective as it is more laborious, time consuming and poses safety hazard. This study focuses in utilizing an Artificial Intelligence (AI) method to automatically classify pavement crack severity. Field data collection was conducted to allow meaningful verification of accuracy and reliability of the crack’s severity prediction based on AI. Several important phases are required in research methodology processes including data collection, image labelling, image resizing, image enhancement, deep convolution neural network (DCNN) training and performance evaluation. Throughout the analysis of image processing results, the image output was successfully classified using MATLAB software. The good agreement between field measurement data and DCNN prediction of crack’s severity proved the reliability of the system. In conclusion, the established method can classify the crack’s severity based on the JKR guideline of visual assessment.

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Pavement Distress Detection With Picucha Methodology for Area-Scan Cameras and Dark Images

Cited by 9 publications

References 13 publications

Experimental and Numerical Research of Paved Microcrack Using Histogram Equalization for Detection and Segmentation

Experimental and Numerical Research of Paved Microcrack Using Histogram Equalization for Detection and Segmentation

Hybrid Human-Artificial Intelligence Approach for Pavement Distress Assessment (Picucha)

Flexible Pavement Crack’s Severity Identification and Classification using Deep Convolution Neural Network

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