Bio: S Meta is an academic researcher from ASELSAN. The author has contributed to research in topics: Signal processing & Algorithm design. The author has an hindex of 1, co-authored 1 publications receiving 57 citations.
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.
Abstract: This paper presents a novel vehicle-classification algorithm that uses the time-variable signal generated by a single inductive loop detector In earlier studies, the noisy raw signal was fed into the algorithm by reducing its size with rough sampling However, this approach loses the original signal form and cannot be the best exemplar vector The developed algorithm suggests three contributions to cope with these problems The first contribution is to clear the noise with discrete Fourier transform (DFT) The second contribution is to transfer the noiseless pattern into the Principal Component Analysis (PCA) domain PCA is exploited not only for decorrelation but for explicit dimensionality reduction as well This goal cannot be achieved by simple raw data sampling The last contribution is to expand the principal components with a local maximum (Lmax) parameter It strengthens the classification accuracy by emphasizing the undercarriage height variation of the vehicle These parameters are fed into the three-layered backpropagation neural network (BPNN) BPNN classifies the vehicles into five groups, and the recognition rate is 9421% This recognition rate has performed best, compared with the methods presented in published works
TL;DR: Experimental results on two fine-grained vehicle datasets demonstrate that the CMP modified CNNs can improve the classification accuracies on the task of fine- grained vehicle classification while a massive amount of parameters are reduced.
Abstract: Convolutional neural networks (CNNs) have recently shown excellent performance on the task of fine-grained vehicle classification, where the motivation is to identify the fine-grained categories of the given vehicles. Generally speaking, the main motivation of the conventional back-propagation algorithm is to optimize the loss function. The algorithm itself does not guarantee if the extracted features are discriminative for the task of classification. Intuitively, if we can learn more discriminative features with a relatively small number of feature maps, the generalization ability of the CNNs will be significantly improved. Therefore, we propose a channel max pooling (CMP) scheme, where a new layer is inserted between the fully connected layers and the convolutional layers. The proposed CMP scheme divides the feature maps into to several sub-groups. Then, it compresses the feature maps within each sub-group into a new one. The compression is carried out by selecting the maximum value among the same locations from different feature maps. Moreover, the proposed CMP layer has the advantage that it can reduce the number of parameters via reducing the number of channels in the CNNs. Experimental results on two fine-grained vehicle datasets demonstrate that the CMP modified CNNs can improve the classification accuracies on the task of fine-grained vehicle classification while a massive amount of parameters are reduced. Moreover, it has competitive performance when comparing with the-state-of-the-art methods.
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.
Abstract: Fine-grained vehicle classification is a challenging topic in computer vision due to the high intraclass variance and low interclass variance. Recently, considerable progress has been made in fine-grained vehicle classification due to the huge success of deep neural networks. Most studies of fine-grained vehicle classification based on neural networks, focus on the neural network structure to improve the classification performance. In contrast to existing works on fine-grained vehicle classification, we focus on the loss function of the neural network. We add a regularization term to the cross-entropy loss and propose a new loss function, Dual Cross-Entropy Loss . The regularization term places a constraint on the probability that a data point is assigned to a class other than its ground-truth class, which can alleviate the vanishing of the gradient when the value of the cross-entropy loss is close to zero. To demonstrate the effectiveness of our loss function, we perform two sets of experiments. The first set is conducted on a small-sample fine-grained vehicle classification dataset, the Stanford Cars-196 dataset. The second set is conducted on two small-sample datasets, the LabelMe dataset and the UIUC-Sports dataset, as well as on one large-sample dataset, the CIFAR-10 dataset. The experimental results show that the proposed loss function improves the fine-grained vehicle classification performance and has good performance on three other general image classification tasks.
TL;DR: This article presents a review of state-of-the-art traffic monitoring systems focusing on the major functionality–vehicle classification and discusses hardware/software design, deployment experience, and system performance of vehicle classification systems.
Abstract: A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality-vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.
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.
Abstract: This paper presents 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. The loop sensor proposed in this paper detects large (e.g., bus) as well as small (e.g., bicycle) vehicles occupying any available space in the roadway, which is the main requirement for sensing heterogeneous and lane-less traffic. To accomplish the sensing of large as well as small vehicles, a multiple loop system with a new inductive loop sensor structure is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle, motor cycle, scooter, car, and bus but also enables accurate counting of the number of vehicles even in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed and tested. Field tests indicate that the prototype successfully detected all types of vehicles and counted, correctly, the number of each type of vehicles. Thus, the suitability of the proposed sensor system for any type of traffic has been established.
01 Mar 2019
TL;DR: A comprehensive study of different coil structures and algorithms for speed and misalignment estimation in a DWPT-VDS, along with their comparison is presented, to maximize theMisalignment detection range for a given size of test coils and provide a robust solution for vehicles with different ground clearances.
Abstract: To overcome the range limitation associated with electric vehicles (EVs), the emerging technology of dynamic wireless power transfer (DWPT) can be employed. Electrified road infrastructure and wirelessly charged vehicle constitute a complex dynamic system whose successful operation requires coordination between the two subsystems and a certain level of knowledge regarding the EV position and speed on the road. A comprehensive vehicular detection system (DWPT-VDS) operating on magnetic principle and intended for DWPT applications is proposed in this paper. The following functionalities are integrated into the DWPT-VDS: a vehicle detection mechanism, the measurement of the vehicle lateral misalignment, vehicle speed measurement, driver information system (DIS), as well as the wireless communication between a roadside power controller and the DIS. When integrated with a DWPT charging system, the DWPT-VDS allows some critical functions, such as correction of the lateral position of the vehicle by the driver, an extended range of full-power reception for a misaligned vehicle, as well as the smooth transition between adjacent pads. This paper presents a comprehensive study of different coil structures and algorithms for speed and misalignment estimation in a DWPT-VDS, along with their comparison. The objective is to maximize the misalignment detection range for a given size of test coils and provide a robust solution for vehicles with different ground clearances. A three-coil system for vehicle misalignment and speed detection is selected as part of a proof-of-concept design. Part of the system is embedded in the road, and the rest is mounted on a wirelessly charged electric bus. The implemented system has been successfully tested in an outdoor environment. A DIS visualizing speed and misalignment information is also developed and tested to help the driver align the vehicle with the road-embedded primary pads.