Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Karballaeezadeh, Nader; Zaremotekhases, Farah; Shamshirband, Shahaboddin; Mosavi, Amir; Nabipour, Narjes; Csiba, Péter; Várkonyi-Kóczy, Annamária R.

doi:10.20944/preprints202001.0227.v1

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…Artificial Intelligence is one of the pioneering technologies being exploited in recent years to provide state-of-the-art mobility solutions. Deep learning-based object detection and identification can be used to provide improved visibility in conditions of degraded vision (such as low light areas, foggy, windy or rainy environments) [ 150 , 151 , 152 ]. IoT-based road condition monitoring, environment monitoring, and surrounding monitoring can provide unprecedented information which can be used to predict future conditions to aid in the desired course of actions.…”

Section: Future Trends On Smart Urban Mobilitymentioning

confidence: 99%

Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Paiva

Ahad

Tripathi

et al. 2021

Sensors

209

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The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encouraging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility-as-a-service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope.

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Section: Future Trends On Smart Urban Mobilitymentioning

confidence: 99%

Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Paiva

Ahad

Tripathi

et al. 2021

Sensors

209

View full text Add to dashboard Cite

show abstract

“…[8][9][10] In the field of structural condition assessment, many deep learning-based techniques have been proposed as an attempt to increase the level of automation of condition inspection, which image analysis-based methods failed to achieve. [11][12][13][14] This includes applications for the detection of defects in infrastructural assets such as cracks in road surfaces, [15][16][17][18][19] bridges 20 and in water and sewerage pipelines. 14,21 Another research field on object detection that also received considerable attention amongst researchers in recent years is the development of deep learning algorithms for post-disaster structural condition identification.…”

Section: Related Workmentioning

confidence: 99%

“…The developed detector that uses a support vector machine (SVM) classifier is trained and tested on vectors of features generated from the input images histograms in addition to another two texture descriptors of non-overlapped square blocks that includes 'patch' and 'no-patch' areas. 18,19 In another similar application, also on detecting road defects, 16 the authors use images captured from a smartphone mounted on a vehicle to detect eight different types of road surface damages. The authors developed a custom, large-scale dataset comprising more than 9000 images containing 15,400 instances of road surface damages to build their detector.…”

Section: Related Workmentioning

confidence: 99%

Deep learning smartphone application for real‐time detection of defects in buildings

Perez

Tah

2021

Struct Control Health Monit

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Condition assessment and health monitoring (CAHM) of built assets requires effective and continuous monitoring of any changes to the material and/or geometric properties of the assets in order to detect any early signs of defects or damage and act on time. Most of the traditional CAHM techniques, however, depend on manual labour despite that, in some cases, the inspection environment can be unsafe and could lead to low efficiency or misjudgement of the severity of the defect. In recent years, computer vision techniques have been proposed as an automated alternative to the traditional CAHM techniques as methods for extracting and analysing feature-related information from asset images and videos. Such methods have proven to be robust and effective solutions, complementary to current time-consuming and unreliable manual observational practices. This work is concerned with the development of a deep learning-based smartphone app, which allows real-time detection of four types of defects in buildings, namely, cracks, mould, stain and paint deterioration. Since smartphones are widely available and equipped with highresolution cameras, this application can offer a practical, low-cost solution for condition assessment procedures of built assets. The obtained results are promising and support the feasibility and effectiveness of the approach to identify and classify various types of building defects. K E Y W O R D Scondition assessment and health monitoring, deep learning, defects in buildings, object detection, smartphone applications | INTRODUCTIONDefects are defined as the deterioration of building features and services to unsatisfactory quality levels of the requirements of the users. 1 Defects and deterioration are common problems in built structures; they are a major concern of buildings and require significant attention. Although various defects are more common in old buildings, they can occur in new buildings as well. 2 Workers' faults, natural causes, poor maintenance and the age of the building are amongst the common causes of defects in built structures. 3 There are many types of building defects. In general, these can be divided into two categories: structural and non-structural defects. Structural defects are those which occur in a

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“…Two primary groups of pavements are Asphalt Concrete (AC) and Portland Cement Concrete 3-Pre-filtering: it identifies parts of the image with high chances of crack occurrence and limits the detection procedure for those areas; doing this reduces the computational expenses and makes the process more efficient. 4-Crack detection: it uses a combination of image processing techniques to separate the crack information from the rest of the image (24).…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

Safaei¹,

Smadi²,

Safaei³

et al. 2021

Preprint

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Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types. This paper proposes a new method that uses an improved version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. This method uses the Gaussian cumulative density function as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of different noise conditions. This method proved to be time and cost-efficient as it took less than 3.15 seconds per 320 × 480 pixels image for a Xeon (R) 3.70 GHz CPU processor to determine the detection results. This makes the model a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of low to severe-level cracks (Accuracy = 97.3%, Precision = 79.21%, Recall= 89.18% and F1 score = 83.9%).

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Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Cited by 15 publications

References 18 publications

Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges

Deep learning smartphone application for real‐time detection of defects in buildings

A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

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