Developing and validating an image processing algorithm for evaluating gravel road dust

Albatayneh, Omar; Forslof, Lars; Ksaibati, Khaled

doi:10.1007/s42947-019-0035-y

Cited by 19 publications

(13 citation statements)

References 21 publications

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“…Further c-Air PM measurements are closely correlated with US Environmental Protection Agency (EPA)-approved standard techniques such as beta-attenuation monitoring (BAM) and tapered element oscillating microbalance (TEOM) [ 73 ]. The neural network–based systems are also employed in automatic detection of road cracks and estimation of the amount of dust on a gravel road which assures the driving safety on the road [ 74 ]. Furthermore, a faster R-CNN model is used to detect the efflorescence and spalling in historical buildings to overcome the manual examination error which may destroy masonry structures [ 75 ].…”

Section: Deep Learning For Smartphone-based Imaging Devicementioning

confidence: 99%

Recent trends in smartphone-based detection for biomedical applications: a review

et al. 2021

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Smartphone-based imaging devices (SIDs) have shown to be versatile and have a wide range of biomedical applications. With the increasing demand for high-quality medical services, technological interventions such as portable devices that can be used in remote and resource-less conditions and have an impact on quantity and quality of care. Additionally, smartphone-based devices have shown their application in the field of teleimaging, food technology, education, etc. Depending on the application and imaging capability required, the optical arrangement of the SID varies which enables them to be used in multiple setups like bright-field, fluorescence, dark-field, and multiple arrays with certain changes in their optics and illumination. This comprehensive review discusses the numerous applications and development of SIDs towards histopathological examination, detection of bacteria and viruses, food technology, and routine diagnosis. Smartphone-based devices are complemented with deep learning methods to further increase the efficiency of the devices.

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Section: Deep Learning For Smartphone-based Imaging Devicementioning

confidence: 99%

Recent trends in smartphone-based detection for biomedical applications: a review

et al. 2021

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“…Traffic volume and composition, climatic conditions, and vehicle speed influence the occurrence of these defects ( 32 ). Dust is generated by traffic, and the quantity generated is influenced by the traffic volume, the gravel material properties, the air velocity near the road surface, and the weather ( 58 , 69 ). Dust particles affect the air and water quality, leading to health problems in humans and animals ( 26 ).…”

Section: Gravel Roads and Their Current Maintenance Practicesmentioning

confidence: 99%

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Mbiyana

Kans

Campos

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Gravel roads form a significant share of the global road network, usually in sparsely populated rural areas. They are important, especially in agriculture, tourism, and forestry, connecting rural to urban areas. This systematic literature study comprises 105 reviewed publications on gravel road maintenance. Review articles on maintenance management practices, especially concerning objective condition assessment and data-driven methods (DDMs), are lacking. Therefore, this review provides a concise overview of current gravel road maintenance practices and ongoing research on objective condition assessment and DDMs for gravel road maintenance. It offers researchers in gravel road maintenance and other related fields a clear indication of where to focus their research efforts, as it suggests the direction for future research. Visual assessment methods are predominant for monitoring the condition of gravel roads, while objective methods and DDMs are not common. Research on gravel roads and their maintenance has increased in the last two decades, especially in North America and Northern Europe. Condition assessment is shifting from subjective to objective methods, utilizing knowledge from technological advancements in image processing, vibration and acoustics analysis, and so forth. There are some excellent research initiatives for objectively assessing the condition of gravel roads and DDMs, but the practical implementation is limited. Implementing objective assessment methods and DDMs generally improves the management of gravel roads with regard to decision-making, maintenance costs, safety, and the stability and comfort of the ride. Objective condition assessment and DDMs have the potential to enhance maintenance practices in the maintenance of gravel roads.

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“…Special focus, particularly in industrialized low-volume roads (LVRs), is directed at the performance and conditions of gravel roads. Traditionally, for low-volume roads, especially gravel roads, route scheduling for maintenance and datacollection purposes is carried out by the local agencies' engineers without a systematic strategy Albatayneh et al, 2019). In such a process, plans and route scheduling can be affected by an individual's personal tendency toward specific goals, criteria and objectives.…”

Section: Introductionmentioning

confidence: 99%

Integrating a Multi-criteria Route Optimization with ArcGIS for Gravel Road Data Collection

Albatayneh¹,

Malkawi²,

Yue³

et al. 2023

JJCE

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The ability to identify and collect low-volume roads’ data, especially gravel roads, is critical in transportation planning. Developing an effective method for gravel-road data collection allows both decision-makers and local agencies to efficiently obtain information regarding road conditions. In this study, a geographical information system (GIS)-based decision- support system was applied to assist local agencies in determining reliable access routes to all gravel roads in Wyoming. The routing criteria were developed using the Network Analyst tool of ArcGIS software based on the restrictions on average daily traffic (ADT) volume values, speed limits, driving distances and driving hours per day. Results indicated that the routing maps created by ArcGIS software were an easy-to-use method to plan and schedule data collection on gravel roads. The routing analysis provided a reliable means to minimize driving time and distance. The methodology developed in this study may be employed by local transportation agencies for road-maintenance purposes in rural areas. KEYWORDS: Low-volume roads, Gravel roads, Route optimization, ArcGIS, Data collection.

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Developing and validating an image processing algorithm for evaluating gravel road dust

Cited by 19 publications

References 21 publications

Recent trends in smartphone-based detection for biomedical applications: a review

Recent trends in smartphone-based detection for biomedical applications: a review

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Integrating a Multi-criteria Route Optimization with ArcGIS for Gravel Road Data Collection

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