A Novel Sensor System for Measuring Wheel Loads of Vehicles on Highways

Zhang, Wenbin; Suo, Chunguang; Wang, Qi

doi:10.3390/s8127671

Cited by 22 publications

(16 citation statements)

References 14 publications

(11 reference statements)

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“…Thus, it is of practical significance to develop a better FEQS system that is free of human labor, low cost, and well-adapted to the operating environment. Currently, there is no suitable solution for non-contact truck weighing; although devices that are more advanced than scales have been developed, contact with vehicles and laborious installation are still required [18,19]. Thus, high cost and limited application is unavoidable in weighing FEQS.…”

Section: Needs For Vision-based Feqs Methodsmentioning

confidence: 99%

Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

et al. 2019

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Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

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Section: Needs For Vision-based Feqs Methodsmentioning

confidence: 99%

Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

et al. 2019

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“…Traditionally, these weights have been collected by pulling the vehicles off the roadway and weighing them at weigh stations while the vehicles are at rest. The static weighing of vehicles in highways has several disadvantages, being time consuming, expensive, and dangerous on heavily travelled roads [ 11,12 ], as shown in the previous chapter.…”

Section: Sensorsmentioning

confidence: 99%

Tire and pavement wear interaction monitoring for road performance indicators

Kaare¹,

Kuhi²,

Koppel³

2012

Estonian J. Eng.

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The design, building and maintenance of national roads constitute a substantial part of the government budget. Taking into account the magnitude and importance of these investments, the expedience, efficiency and sustainability of roads are of great public interest and their performance should be measured. For performance assessment of roads or road sections it is important to gather data about different key performance indicators. Flexible pavements deteriorate under traffic loads and climate effects. This effect depends on the technology and materials of the road, but the greatest effects depend on traffic loads and volumes. Systems can be developed to give information about tire wearing and its impact on the pavement wearing. To ensure traffic safety, especially in countries with cold climate, it is very important to gather continuously data about tire tread wear. New technologies have made it possible to integrate the measuring of tire tread wear and tire type on roads continuously and present new opportunities to monitor and analyse conditions of tires. This paper is part of a road performance measurement system presenting a concept for input data collecting.

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“…Pratiksha Bhuta, Karan Desai, Archita Keni. [4]:-This system is aimed at making vehicle driving safer than before. This is implemented using Arduino.…”

Section: Literature Reviewmentioning

confidence: 99%

Portable Vehicle Health Management Sensor and Its Application in Transportation

2017

IJMTER

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In recent years, due to the expansion of the vehicles transportation System and concerns about the lack of accurate calculations of vehicle Weight, a system that is able to calculate the vehicles weight at any Moment, it seems necessary. Overloading is a big problem in the goods transport sector.Overweight vehicles lead to all sorts of negative issues. The generic structure of Portable Vehicle Weight Measurement System is presented, followed by detailed module structure description, including several aspects, like communication, measurement method and power consumption. In this, we provide design and implementation of modern systems that calculate accurate weight of vehicle at any time and also we develop a mobile application which will help us to fetch the details of vehicle . Because of mobile application transparency and flexibility will be provided. we develop a data transfer system that provides communication between computer and sensor for Achieving accuracy and safety. It will saving time of RTO Process, for Easy process as data verification, Pollution control and so on.

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A Novel Sensor System for Measuring Wheel Loads of Vehicles on Highways

Cited by 22 publications

References 14 publications

Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

Tire and pavement wear interaction monitoring for road performance indicators

Portable Vehicle Health Management Sensor and Its Application in Transportation

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