Weigh-in-motion Using Machine Learning and Telematics

Kirushnath, Sivaramalingam; Kabaso, Boniface

doi:10.1109/tafgen.2018.8580463

Cited by 8 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some eating tools including spoons, forks, or chopsticks were considered to measure the weight of food in an image using several image processing techniques and the exchangeable image file (EXIF) metadata [20]. Telematics data was combined with machine learning to determine the weight of vehicles on a road segment [21].…”

Section: Literature Reviewmentioning

confidence: 99%

An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

Tran

Kim

Nguyễn

2023

Adv. technol. innov.

View full text Add to dashboard Cite

AI impacts surrounding human life, such as the economy, health, education, and agricultural production; however, the crop prices in the harvest season are still on manual calculation, which causes doubts about accuracy. In this study, an image-based approach is proposed to help farmers calculate rice prices more accurately. YOLOv5 is used to detect and extract the scales in the images taken from the harvesting of rice crops. Then, various image processing techniques, such as brightness balance, background removal, etc., are compiled to determine the needle position and number on the extracted scale. Lastly, geometric transformations are proposed to calculate the weight. A real dataset of 709 images is used for the experiment. The proposed method achieves good results in terms of mAP@0.5 at 0.995, mAP@[0.5:0.95] at 0.830 for scale detection, and MAE at 3.7 for weight calculation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

Tran

Kim

Nguyễn

2023

Adv. technol. innov.

View full text Add to dashboard Cite

show abstract

“…Since the 2000s, there has been a surge in interest in the application of machine learning to WIM and its related problems, corresponding to significant increases in the performance of artificial intelligence techniques. For example, machine learning methods have been used to predict potential vehicle overload from telematics such as speed, engine speed, and road gradient (Kirushnath & Kabaso, 2018). Other research extends bridge WIM as a sub-problem to help identify bridge damage using machine learning (Gonzalez & Karoumi, 2015), although the technique was only used in railway networks where vehicle axle spacing is known.…”

Section: Fig 3: Nine Truck Types Used In This Paper Adapted Frommentioning

confidence: 99%

Machine learning approaches to determining truck type from bridge loading response

Wang¹,

Flood²

2023

ITcon

View full text Add to dashboard Cite

The paper is concerned with the development and comparison of alternative machine learning methods of determining the type of truck crossing a bridge from the dynamic response it induces within the bridge structure, the so-called weigh-in-motion problem. Weigh-in-motion is a rich engineering problem presenting many challenges for current machine learning technologies, and for this reason is proposed as a benchmark for guiding and assessing advances in the application of this field of artificial intelligence. A review is first provided of existing methods of determining truck types and loading attributes using both machine learning and heuristic search techniques. The most promising approach to date, that of artificial neural networks, is then compared to support vector machines in a comprehensive study considering a range of configurations of both modeling techniques. A local scatter point smoothing schema is adopted as a means of selecting an optimal set of design parameters for each model type. Three main model formats are considered: (i) a monolithic model structure with a one-versus-all truck type classification strategy; (ii) an array of sub-models each dedicated to one truck type with a one-versus-all classification strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks in a one-versus-one classification strategy. Overall, the formats that used an array of sub-models performed best at truck classification, with the support vector machines having a slight edge over the artificial neural networks. The paper concludes with some suggestions for extending the work to a broader scope of problems.

show abstract

“…An omnipresent WIM system capable of monitoring the payload of the vehicle in any road segment could help the transport industry in many ways. In [12], we proposed an approach for a new WIM system using the VT data and ML. We discussed the prototype design and development of a new WIM system using the proposed approach.…”

Section: The Prototype Wim Systemmentioning

confidence: 99%

“…• Awarene ss of Problem [11] to identify the gaps in the use of Intelligent Transport System (ITS) and identified the problem in transportation, then a conceptual framework was proposed in [12]. In the solution design phase, we developed the proposed system architecture, design the solution artefact as a prototype, built it.…”

Section: Problem Identificationmentioning

confidence: 99%

Recording and Reporting a DSR Using C-K Design Theory

Sivaramalingam¹,

Kabaso²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Concept Knowledge (C-K) theory has been used in engineering and science-based research for more than a decade. Design of an Information Technology (IT) artefact is mostly pragmatic in nature. Design Science Research (DSR) methodology applied and studied in many Information Systems (IS) research. Many sub design decisions involved through the design of an IT artefact from a concept (idea) to a working prototype. A DSR artefact is based on a combination of decisions made during several sub-design stages. Artefacts are built based on the selection of elements in each sub-design space. Recording the design decisions on each sub-design space would be beneficial for future researchers. By knowing the design decisions on each sub-design space, researchers would be able to try different combinations of the design. C-K theory provides the ability to capture the design processes’ several sub-design spaces. In this paper, we discuss the DSR research methodology by looking at the stages proposed in the literature, and the application of C-K theory in an IT-based DSR. This paper also proposed a C-K theory-based protocol called Concept Tree for tracking and reporting artefact design steps. The application of C-K theory in DSR is exhibited using the implementation of the Concept Tree for a prototype design IT artefact.

show abstract

Weigh-in-motion Using Machine Learning and Telematics

Cited by 8 publications

References 30 publications

An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

Machine learning approaches to determining truck type from bridge loading response

Recording and Reporting a DSR Using C-K Design Theory

Contact Info

Product

Resources

About