A novel system developed based on image processing techniques for dynamical measurement of tire-surface contact area

Derafshpour, Sajjad; Valizadeh, Morteza; Mardani, Aref; Saray, Masoud Tamaddoni

doi:10.1016/j.measurement.2019.02.074

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…otsu T denotes the threshold of Otsu's method calculated on k D . More details about touching and non-touching pixels classification can be found in our previous work [13]. After binarization of difference image, the image B is processed by opening morphological filter to eliminate those pixels erroneously classified as touching pixels, also an appropriate closing morphological filter is used to fill the holes appeared in touching regions because of LED light reflection and tire tread.…”

Section: Max(75t )mentioning

confidence: 99%

A dynamic model for estimation of the tire contact patch by using machine vision techniques

Derafshpour

Mardani

Valizadeh

2023

Preprint

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The maximum acceleration that can be achieved by the car is limited by two main factors of maximum torque in the drive wheels and the maximum traction force in the contact patch. The first factor depends on the performance of the engine and the transmission system in the drivetrain dynamics, and the second factor depends on the friction between the tire and the contact patch. The importance of investigating the contact patch is valuable from perspective of increasing traction efficiency to reducing fuel consumption. In this study, a novel method based on image processing techniques was utilized to dynamically measure contact patch. Then, contact patch was modeled by the Bat algorithm considering the load on the tire and inflation pressure. Finally, the model was evaluated visually and statistically. Coefficient of determination (R2) for this model was 0.95.

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Section: Max(75t )mentioning

confidence: 99%

A dynamic model for estimation of the tire contact patch by using machine vision techniques

Derafshpour

Mardani

Valizadeh

2023

Preprint

Self Cite

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“…Compared to the current scientific research document that addresses the depth of wheel tracks, little information has been provided on the recognition of wheel tracks exceeding the circumference of a single tire in the agriculture, forestry, and road transportation industries. However, recognition of the tiny fragment of wheel tracks (area of wheel contact surface) has been reported by Derafshpour 31 . The rate of soil compaction caused by vehicle traffic and the location of the wheel track may be determined easily by recognizing the wheel track, which Ma 32 has testified in their studies through recognition of the wheel track caused by snow terrain vehicles in the snow.…”

Section: Introductionmentioning

confidence: 99%

Identification of wheel track in the wheat field

Zhang

2024

Sci Rep

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Agriculture machinery navigating along permanent traffic lanes in the farmland may avoid causing extensive soil compaction. However, the permanent traffic lanes are frequently covered up or eliminated by following tillage practices. It is necessary to identify the wheel tracks designed as permanent traffic lanes in order to ensure the agriculture machinery travels along the designated wheel tracks when cultivating the field. This study proposed an identification method of wheel tracks based on the morphological characteristics of wheel tracks and the environmental conditions around the wheel tracks in the wheat fields. The proposed method first utilized the maximum interclass variance to identify the contours of the main part of the wheel track and the shadow regions around the wheel track’s edges. The main part of the wheel tracks was then separated from interference pixels by moving the centerline of the main part of the wheel track, which was derived by skeleton algorithm and curve fitting, towards the right or left edge of the wheel track at a specific distance. In a morphological opening operation, specific linear and circular structural elements were used to segment the shadow regions along the edge of the wheel track. The remaining wheel track was finally recognized by computing the complement of the region identified. After achieving the segmentation of wheel tracks, many reference points near the outside of the wheel track edge in the original image were chosen as fiducial points for evaluating the differences between the actual value and the recognized wheel track edge. The evaluation was based on computing the root mean squared error (RMSE) and the mean absolute error (MAE) of coordinates of reference points and recognized wheel track edge. The results showed that the largest RMSE and MAE were 24.01 pixels (0.0045 m) and 17.32 pixels (0.0032 m), respectively. The low values of RMSE and MAE reveal that the accuracy of the algorithm developed in this study is high, and using this algorithm may segment the wheel track in the wheat field accurately.

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“…The same technique has been used in various fields of science, e.g. real-time carbon and coal particle size distribution (PSD) measurement, monitoring of flame behavior and combustion performance, and measurement of a contact area criterion in vehicle performance [31,32,33,34]. By applying the tracking procedure, it was possible to track particles with equal time intervals and to determine their displacement in a very efficient and accurate way.…”

Section: Introductionmentioning

confidence: 99%

Towards application of uncertainty quantification procedure combined with experimental procedure for assessment of the accuracy of the DEM approach dedicated for granular flow modeling

et al. 2022

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A novel system developed based on image processing techniques for dynamical measurement of tire-surface contact area

Cited by 20 publications

References 17 publications

A dynamic model for estimation of the tire contact patch by using machine vision techniques

A dynamic model for estimation of the tire contact patch by using machine vision techniques

Identification of wheel track in the wheat field

Towards application of uncertainty quantification procedure combined with experimental procedure for assessment of the accuracy of the DEM approach dedicated for granular flow modeling

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