Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field

Wang, Peng-Hui; Hu, Yongbiao; Dai, Yong; Tian, Mingrui

doi:10.1155/2017/1604130

Cited by 55 publications

(32 citation statements)

References 20 publications

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“…Recently, the automatized acquisition and processing of spatial data on the distortions of roads and runways have been a focus in many studies [28][29][30][31][32][33][34]. In the studies, the authors focused mainly on the interpretation and analysis of road and runway records.…”

Section: Existing Information Models For Monitoring Distortion On Runmentioning

confidence: 99%

Improved Decision-Making Geo-Information System for Continuous Monitoring of Deformations on Airport Infrastructure

Doler

Kovačić

2018

IJGI

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Heavy burdens are placed on runways daily; thus, it is necessary to monitor these surfaces regularly. The appearance of distortions can cause difficulties for aircraft when they land or take off, which occurs mainly for lighter planes. This can be seen in reduced grip, uneven use of tires, problems with measurement systems, and unbalanced drive, which can result in damage to or even destruction of the plane or its parts. For that purpose, the model of continuous monitoring of distortions on and under the surface of the runway was created. This model is based on geodetic and geophysical methods. The method for monitoring distortions was developed into a decision-making model in the shape of a geo-information base. The results provide an image of the state of the runway including damage on and under the surface, which are the most common causes of runway distortions. The data are automatically recorded in the newly established information system, which supports the decision making regarding renovations and runway maintenance. The measurement on the runway takes 30 min, which does not lead to a long closure of the airport and, thus, reduces traffic jams and additional costs.

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Section: Existing Information Models For Monitoring Distortion On Runmentioning

confidence: 99%

Improved Decision-Making Geo-Information System for Continuous Monitoring of Deformations on Airport Infrastructure

Doler

Kovačić

2018

IJGI

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“…This is one of the advantages of analyzing the condition of the pavement with accelerometer data compared to computer vision based methods. Computer vision based methods are able to accurately detect the location of a road anomaly whatever the depth or severity but the irregularity cannot be fully captured and analyzed using computer vision based methods alone [6][7][8][9][10].…”

Section: Signal Processingmentioning

confidence: 99%

Pavement Condition Analysis via Vehicle Mounted Accelerometer Data

Mohiddeen

Rosli²,

Hanif³

et al. 2020

IIUMEJ

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Road anomalies and irregularities such as potholes and uneven surfaces are a common hazard in South East Asia and developing countries. Such hazards pose a threat to the safety and well-being of both civilians going about their daily routine and tourists who are exploring the city. Since bicycles and rickshaws are still a common mode of transport used by both civilians and tourists in many South East Asian countries, it is essential to improve the overall quality and smoothness of pavements which are traversed by these vehicles. Management of international sporting and recreational events also require satisfactory road and pavement conditions. Before pavement conditions can be improved, it is an essential prerequisite to obtain comprehensive information about road irregularities such as the location and also severity of the road irregularity (depth of the potholes and height of bumps). In this paper, we propose a method for obtaining mathematical models that represent the overall condition of the pavements that are part of a commonly traversed cycling route. Such mathematical models and coefficients can be stored in the cloud of an Internet of Things (IOT) data analytics systems subsequently leading to identification of regions with severe road irregularities. ABSTRAK: Kerosakan pada permukaan jalan raya merupakan salah satu faktor risiko kemalangan yang berlaku secara meluas di negara Asia Tenggara dan negara membangun yang lain. Memandangkan kenderaan seperti beca dan basikal masih diguna pakai secara meluas di negara membangun, adalah mustahak untuk membaik pulih kerosakan jalan raya. Pengurusan sukan antarabangsa dan rekreasi juga memerlukan keadaan jalan dan laluan pejalan kaki yang baik. Sebelum kerja membaik pulih dapat dilakukan, maklumat lengkap mengenai tahap kerosakan jalan raya dan lokasi kerosakan diperlukan. Dalam kajian ini satu kaedah telah diperkenalkan untuk mendapatkan persamaan matematik yang menggambarkan keadaan sebenar permukaan jalan raya, di mana sebahagiannya merupakan laluan berbasikal yang selalu digunakan. Model matematik dan pekali ini boleh di simpan dalam sistem analisis data awan Internet Benda (IOT) kemudiannya dapat mengenal pasti kawasan jalan yang rosak dan tidak rata.

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“…In the other hand, many of the research attempts such as the work by K. Azhar [9] focuses on the traditional image processingmechanisms and further extraction of the parameter some of the other research attempts like the work by P. Wang [10] have applied segmentation methods for identification of the defect objects on the image.…”

Section: Parallel Research Outcomesmentioning

confidence: 99%

Parametric Extraction of the Road Conditions Spatial Data and Detection of Defeats using Pragmatic Clustering Method

Kumari¹,

Govardhan²

2020

IJEAT

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Growth in the population and road transportation for any region can create a higher demand for better road conditions and the less safe road conditions can be a great bottleneck for the growth of the nation. Hence most of the progressive nations like India,builds better infrastructure for the road transportations.Nevertheless,the higher populations in countries like India,demand greater maintenance of the roads and for a gigantic demographic, the maintenance work can be very tedious. Also,the damage in the road surfaces can also lead to the increasing rate of the accidents. Henceforth,the demand of the road maintenance is always increasing.Nevertheless, the existing road maintenanceauthorities deploy a manual process for identification of the repair needs, which is naturally highly time consuming. Thus, this work identifies the demand for automation of the road condition monitoring system and identifies the defects based on three classes as cracks, patch works and potholes on the road surface. The work deploys a novel model for parametric extraction, in order to segregate the defect types. The segregation of the defect types can be highly challenging due to the nature of the data,which clearly hints to solve the problem using unsupervised methods. Thus, this work also deploys a pragmatic clustering method using a decisive factor,which is again generated from the extracted features of parameters. The work demonstrates nearly 96% accuracy on the benchmarked dataset with sophistication on the complexity of the model.

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Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field

Cited by 55 publications

References 20 publications

Improved Decision-Making Geo-Information System for Continuous Monitoring of Deformations on Airport Infrastructure

Improved Decision-Making Geo-Information System for Continuous Monitoring of Deformations on Airport Infrastructure

Pavement Condition Analysis via Vehicle Mounted Accelerometer Data

Parametric Extraction of the Road Conditions Spatial Data and Detection of Defeats using Pragmatic Clustering Method

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