Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification

There is a drastic increase experienced in the production of vehicles in recent years across the globe.In this scenario,vehicle classification system plays a vital part in designing Intelligent Transportation Systems(ITS)for automatic highway toll collection,autonomous driving,and traffic management.Recently,computer vision and pattern recognition models are useful in designing effective vehicle classification systems.But these models are trained using a small number of hand-engineered features derived fromsmall datasets.So,such models cannot be applied for real-time road traffic conditions.Recent developments in Deep Learning(DL)-enabled vehicle classification models are highly helpful in resolving the issues that exist in traditional models.In this background,the current study develops a Lightning Search Algorithm with Deep Transfer Learning-based Vehicle Classification Model for ITS,named LSADTL-VCITS model.The key objective of the presented LSADTL-VCITS model is to automatically detect and classify the types of vehicles.To accomplish this,the presented LSADTL-VCITS model initially employs You Only Look Once(YOLO)-v5 object detector with Capsule Network(CapsNet)as baseline model.In addition,the proposed LSADTL-VCITS model applies LSA with Multilayer Perceptron(MLP)for detection and classification of the vehicles.The performance of the proposed LSADTL-VCITS model was experimentally validated using benchmark dataset and the outcomes were examined under several measures.The experimental outcomes established the superiority of the proposed LSADTL-VCITS model compared to existing approaches.

A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

Image classification is a core field in the research area of image proces-sing and computer vision in which vehicle classification is a critical domain.The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security,traffic analysis,and self-driving and autonomous vehicles.The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional,and handcrafted means of solving image analysis problems.In this paper,a combina-tion of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme,particle swarm optimization(PSO),was employed for autonomous vehi-cle classification.The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented.The trained model was classified using several classifiers;however,the Cubic SVM(CSVM)classifier was found to out-perform the others in both time consumption and accuracy(94.8%).The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accu-racy(94.8%)but also in terms of training time(82.7 s)and speed prediction(380 obs/sec).

An end-to-end convolutional network for joint detecting and denoising adversarial perturbations in vehicle classification

Deep convolutional neural networks(DCNNs)have been widely deployed in real-world scenarios.However,DCNNs are easily tricked by adversarial examples,which present challenges for critical applications,such as vehicle classification.To address this problem,we propose a novel end-to-end convolutional network for joint detection and removal of adversarial perturbations by denoising(DDAP).It gets rid of adversarial perturbations using the DDAP denoiser based on adversarial examples discovered by the DDAP detector.The proposed method can be regarded as a pre-processing step—it does not require modifying the structure of the vehicle classification model and hardly affects the classification results on clean images.We consider four kinds of adversarial attack(FGSM,BIM,DeepFool,PGD)to verify DDAP’s capabilities when trained on BIT-Vehicle and other public datasets.It provides better defense than other state-of-the-art defensive methods.

Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

Recently,sparse representation classification(SRC)and fisher discrimination dictionary learning(FDDL)methods have emerged as important methods for vehicle classification.In this paper,inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection,we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors.To improve the classification accuracy in complex scenes,we develop a new method,called multi-task joint sparse representation classification based on fisher discrimination dictionary learning,for vehicle classification.In our proposed method,the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients(MFCC).Moreover,we extend our model to handle sparse environmental noise.We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.

Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

Thailand has been on the World Health Organization(WHO)’s notorious deadliest road list for several years,currently ranking eighth on the list.Among all types of road fatalities,pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures,such as safety belts.Passenger overloading is illegal,but it is often overlooked.The country often uses police checkpoints to enforce traffic laws.However,there are few or no highway patrols to apprehend offending drivers.Therefore,in this study,we propose the use of existing closed-circuit television(CCTV)traffic cameras with deep learning techniques to classify overloaded public transport pickup trucks(PTPT)to help reduce accidents.As the said type of vehicle and its passenger occupancy characteristics are unique,a new model is deemed necessary.The contributions of this study are as follows:First,we used various state-of-the-art object detection YOLOv5(You Only Look Once)models to obtain the optimum overcrowded model pretrained on our manually labeled dataset.Second,we made our custom dataset available.Upon investigation,we compared all the latestYOLOv5 models and discovered that theYOLOv5L yielded the optimal performance with a mean average precision(mAP)of 95.1%and an inference time of 33 frames per second(FPS)on a graphic processing unit(GPU).We aim to deploy the selected model on traffic control computers to alert the police of such passenger-overloading violations.The use of a chosen algorithm is feasible and is expected to help reduce trafficrelated fatalities.

Who are on the road? A study on vehicle usage characteristics based on one-week vehicle trajectory data

Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use.Thanks to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns.In this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles.Wefind that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles.The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.

Review of control and guidance technology on hypersonic vehicle

Air-breathing hypersonic vehicle has great military and potential economic value due to its characteristics:high velocity,long range,quick response.Therefore,the development of hypersonic vehicle and its guidance and control technology are reviewed in this paper.Firstly,the development and classification of hypersonic vehicles around the world are summarized,and the geometric configuration and mission profile of typical air-breathing hypersonic vehicle are given.Secondly,the control difficulties of air-breathing hypersonic vehicle are introduced,including integrated design of engine and fuselage,static instability,strong nonlinearity,uncertain aerodynamic parameters,etc.According to its control requirements,the control methods considering external disturbance,fault-tolerant control methods,anti-saturation methods,and prescribed performance control methods considering transient performance constraints are summarized respectively.The classification and comparison of various control methods are given,and the frontiers of theoretical development are analyzed.Finally,considering the effects of composite disturbances,the design of terminal guidance law under multiple constraints is overviewed,including guidance law with angle constraint,velocity constraint,acceleration constraint and time constraint.Similarly,the classification of guidance law design methods under different constraints,their advantages as well as the future development trend and requirements are introduced.

Experimental analysis of operational data for roundabouts through advanced image processing

Roundabout is still the focus of several investigations due to the relevant number of variables affecting their operational performances(i.e.,capacity,safety,emissions).To develop reliable models,investigations should be supported by devices and relate d sensors to extract variables of interest(i.e.,flow,speed,gap,lag,follow-up time,vehicle classification and trajectory).Notwithstanding that several sensors and technolo gies are currently used for data collection,most of them present limitations.The paper presents the investigation carried out to survey vehicle movem ents at roundabouts as a comprehensive video image analysis system is able to derive the origin/destination(O/D)matrix,compile a vehicle classification,track individual vehicle trajectories together with corresponding speeds and accelerations along paths.To this end,the authors collected video-sequences that were analysed with a piece of software developed for that task.To minimize the problems due to perspective distortion,environmental effects,and obstructions,a number of camera set-up configurations were adopted with equipment being placed on central or external poles,and on permanent fixtures such as raised working platforms outside the confines of the intersection area.Performance of those installation set-ups with different vehicle tracking strategies has been evaluated.Particularly,speed has been successfully related to trajectory tortuosity,the result of which emphasizes the tremendous potential of image analysis and opens up to further studies on the evaluation of the operational effects of roundabout geometrics.