首页|Data from Southeast University Provide New Insights into Machine Learning (Non-contact Vehicle Weight Identification Method Based On Explainable Machine Learning Models and Computer Vision)
Data from Southeast University Provide New Insights into Machine Learning (Non-contact Vehicle Weight Identification Method Based On Explainable Machine Learning Models and Computer Vision)
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Springer Nature
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating in Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “This paper first explores an alternative non-contact method based on computer vision and explainable machine learning (EML) models to identify and predict vehicle overload cost-effectively. First, 1108 sets of data are extracted from traditional contact measurements, non-contact measurements (Optical Character Recognition and thermal imaging), and literature collection to establish a novel and comprehensive database.” Financial supporters for this research include Transportation Science and Technology Project of Jiangsu Province, Tencent Foundation, Natural Science Foundation of Jiangsu Province, Key Scientific and Tech- nological Projects of Jiangxi Provincial Department of Transportation, State Key Laboratory of Mechanical Behavior. The news reporters obtained a quote from the research from Southeast University, “The missing value imputation and the randomized search are then selected to find the optimal ML model for further analysis. Moreover, two typical theoretical and five ML models are adopted to evaluate the optimal model’s perfor- mance. Finally, the sHapley Additive exPlanations (SHAP) is applied to interpret the influence factors of the optimal ML model. The results indicate that the divided length between the tire and the ground is the most significant input feature, followed by the tire’s inflation pressure, the section height of tire, and the radius.”
NanjingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningSoutheast University
NETL
NSTL
Springer Nature
