Journal ArticleDOI
Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks
Yong-Kul Ki,Doo-Kwon Baik +1 more
TLDR
A new algorithm for ILD using back-propagation neural networks is suggested to improve the vehicle-classification accuracy compared to the conventional method based on ILD.Abstract:
Vehicle class is an important parameter in the process of road-traffic measurement. Currently, inductive-loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve the accuracy, the authors suggest a new algorithm for ILD using back-propagation neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and frequency waveform. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.5%. The results verified that the proposed algorithm improves the vehicle-classification accuracy compared to the conventional method based on ILDread more
Citations
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Journal ArticleDOI
Dual Cross-Entropy Loss for Small-Sample Fine-Grained Vehicle Classification
TL;DR: This work adds a regularization term to the cross-entropy loss and proposes a new loss function, Dual Cross-Entropy Loss, which improves the fine-grained vehicle classification performance and has good performance on three other general image classification tasks.
Journal ArticleDOI
Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors
TL;DR: A new sensing device that can simultaneously monitor traffic congestion and urban flash floods is presented, based on the combination of passive infrared sensors and ultrasonic rangefinder, and relies on dynamic Bayesian Networks to fuse heterogeneous data both spatially and temporally for vehicle detection.
Journal ArticleDOI
A Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic
TL;DR: A novel inductive loop sensor that can detect vehicles under a heterogeneous and less-lane-disciplined traffic and thus can be used to support a traffic control management system in optimizing the best use of existing roads is presented.
Journal ArticleDOI
Vehicle-Classification Algorithm Based on Component Analysis for Single-Loop Inductive Detector
S Meta,Muhammed Cinsdikici +1 more
TL;DR: A novel vehicle-classification algorithm that uses the time-variable signal generated by a single inductive loop detector to strengthen the classification accuracy by emphasizing the undercarriage height variation of the vehicle.
Journal ArticleDOI
Estimating Real-Time Traffic Carbon Dioxide Emissions Based on Intelligent Transportation System Technologies
TL;DR: The results of the case study indicate that ITS technologies can be a useful tool for real-time estimations of CO2 emissions with a high spatiotemporal resolution.
References
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Journal ArticleDOI
Detection and classification of vehicles
TL;DR: Algorithm for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence is presented.
Proceedings ArticleDOI
A vehicle classification based on inductive loop detectors
TL;DR: In this article, the influence of loop length (in direction of vehicle movement) on differences between characteristics describing the magnetic profiles of the vehicles belonging to the different classes is discussed, and the case of extremely short loop (10 cm) which allows detection of the number of axles is also analyzed.
Journal ArticleDOI
Using Dual Loop Speed Traps To Identify Detector Errors
TL;DR: In this paper, a formal methodology for testing speed traps off-line has been developed, and ways to extend the work to on-line testing are suggested, and the work is used to evaluate several loop sensor units, revealing problems in two models.
Journal ArticleDOI
Model for accurate speed measurement using double-loop detectors
Yong-Kul Ki,Doo-Kwon Baik +1 more
TL;DR: A new model that uses an error-filtering algorithm to improve the accuracy of speed measurements and it can be concluded that the proposed model significantly improves vehicle-speed-measuring accuracy.
Proceedings ArticleDOI
A method of vehicle classification using models and neural networks
Wei Wu,Zhang QiSen,Wang Mingjun +2 more
TL;DR: Experimental results show that the parameterized model can satisfactorily and effectively describe vehicles, and the correct rate for vehicle recognition using neural networks classifier is more than 91%.