Road surface conditions forecasting in rainy weather using artificial neural networks

Kim, Sangyoup; Lee, Jong-Hak; Yoon, Taekwan

doi:10.1016/j.ssci.2021.105302

Cited by 22 publications

(10 citation statements)

References 10 publications

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“…In final, the KNN classifier produces better accuracy around 78% than the Naïve Bayes classifier around 72%. This result is following previous research regarding road surface conditions, which were discussed with a different perspective [6], [23], where they stated that the KNN classifier is…”

Section: Resultssupporting

confidence: 88%

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Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

Marianingsih¹,

Widodo²,

Pieter³

et al. 2022

JMEST

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The mechanized ability to specify the way surface type is a piece of key enlightenment for autonomous transportation machine navigation like wheelchairs and smart cars. In the present work, the extracted features from the object are getting based on structure and surface evidence using Gray Level Co-occurrence Matrix (GLCM). Furthermore, K-Nearest Neighbor (K-NN) Classifier was built to classify the road surface image into three classes, asphalt, gravel, and pavement. A comparison of KNN and Naïve Bayes (NB) was used in present study. We have constructed a road image dataset of 450 samples from real-world road images in the asphalt, gravel, and pavement. Experiment result that the classification accuracy using the K-NN classifier is 78%, which is better as compared to Naïve Bayes classifier which has a classification accuracy of 72%. The paving class has the smallest accuracy in both classifier methods. The two classifiers have nearly the same computing time, 3.459 seconds for the KNN Classifier and 3.464 seconds for the Naive Bayes Classifier.

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Section: Resultssupporting

confidence: 88%

“…The confusion matrix consists of four basic characteristics (value) that are used to define the measurement metrics of the classifier. These four values are recall, precision, F-measure, and accuracy [23]. An example of a confusion matrix is shown in Table 1 Percentage of related sides that are properly intelligible Recall.…”

Section: Resultsmentioning

confidence: 99%

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

Marianingsih¹,

Widodo²,

Pieter³

et al. 2022

JMEST

View full text Add to dashboard Cite

show abstract

“…The techniques used in weather forecasting also vary greatly depending on past and present science and technology, including numerical techniques [7], [8], which use large-scale computers [9], then developed using machine learning techniques with linear regression [4], artificial neural networks [10]- [17] and deep learning [5][18]- [21]. The disadvantage of using linear regression in weather forecasting is that linear regression as a high variation model is because it is not stable for outliers so that to improve it, more data is needed.…”

Section: Introductionmentioning

confidence: 99%

Weather Forecasting Analysis using Bayesian Regularization Algorithms

Intan

2022

ijicom

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Weather forecasting has become very urgent in various fields of human life, including in big cities. The need for weather forecasting accuracy will be effective and efficient in managing the quality of civilization flexibly. Bayesian regularization is one of the techniques used to obtain accurate results and development of artificial neural networks. The training process achieves the smallest epoch using a general processing unit to solve big data and high resolution. Scenarios performed via dataset partitioning and MSE enhancement. The addition of training data will improve system performance which indicates a significant increasing accuracy. Likewise, the decrease in MSE can increase the system accuracy to achieve a convergence stability point. Weather forecasting can recommend work units within the city and its surroundings, even between provinces or countries.

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“…In the literature, Ref. [10] exploited the data from a weather station to forecast the road surface condition in rainy days. Ref.…”

Section: Introductionmentioning

confidence: 99%

Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles

et al. 2022

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Currently, road surface conditions ahead of autonomous vehicles are not well detected by the existing sensors on those autonomous vehicles. However, driving safety should be ensured for the weather-induced road conditions for day and night. An investigation into deep learning to recognize the road surface conditions in the day is conducted using the collected data from an embedded camera on the front of the vehicles. Deep learning models have only been proven to be successful in the day, but they have not been assessed for night conditions to date. The objective of this work is to propose deep learning models to detect on-line road surface conditions caused by weather ahead of the autonomous vehicles at night with a high accuracy. For this study, different deep learning models, namely traditional CNN, SqueezeNet, VGG, ResNet, and DenseNet models, are applied with performance comparison. Considering the current limitation of existing night-time detection, reflection features of different road surfaces are investigated in this paper. According to the features, night-time databases are collected with and without ambient illumination. These databases are collected from several public videos in order to make the selected models more applicable to more scenes. In addition, selected models are trained based on a collected database. Finally, in the validation, the accuracy of these models to classify dry, wet, and snowy road surface conditions at night can be up to 94%.

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Road surface conditions forecasting in rainy weather using artificial neural networks

Cited by 22 publications

References 10 publications

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

Weather Forecasting Analysis using Bayesian Regularization Algorithms

Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles

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