An Ensemble Deep Learning Method for Vehicle Type Classification on Visual Traffic Surveillance Sensors

This paper proposes "An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an Internet of Things (IoT) Gateway and Deep Learning" that collects information from the sensors of an autonomous vehicle, diagnoses itself, and the influence between its parts by using Deep Learning and informs the driver of the result. The ISS consists of three modules. The first In-Vehicle Gateway Module (In-VGM) collects the data from the in-vehicle sensors, consisting of media data like a black box, driving radar, and the control messages of the vehicle, and transfers each of the data collected through each Controller Area Network (CAN), FlexRay, and Media Oriented Systems Transport (MOST) protocols to the on-board diagnostics (OBD) or the actuators. The data collected from the in-vehicle sensors is transferred to the CAN or FlexRay protocol and the media data collected while driving is transferred to the MOST protocol. Various types of messages transferred are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors and reasons the risk of the vehicle parts and consumables and the risk of the other parts influenced by a defective part. It diagnoses the vehicle's total condition risk. The third Data Processing Module (DPM) is based on Edge Computing and has an Edge Computing based Self-diagnosis Service (ECSS) to improve the self-diagnosis speed and reduce the system overhead, while a V2X based Accident Notification Service (VANS) informs the adjacent vehicles and infrastructures of the self-diagnosis result analyzed by the OBD. This paper improves upon the simultaneous message transmission efficiency through the In-VGM by 15.25% and diminishes the learning error rate of a Neural Network algorithm through the ODLM by about 5.5%. Therefore, in addition, by transferring the self-diagnosis information and by managing the time to replace the car parts of an autonomous driving vehicle safely, this reduces loss of life and overall cost.

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“…The proposed method is able to enhance the mean precision of all the categories, in the condition of high overall accuracy [30].…”

Section: Fault Diagnosis Of a Vehiclementioning

confidence: 97%

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

et al. 2018

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“…The classification accuracy was 97.8%. The imbalanced dataset problem was also addressed by Liu et al [80]. Specifically, to increase the number of samples for certain vehicle types, they apply various data augmentation techniques such as random rotation, cropping, flips, and shifts and created an ensemble of CNN models based on the parameters obtained from the augmented dataset.…”

Section: Over-roadway-based Vehicle Classificationmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

153

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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 the 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.

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“…computer vision, machine interpretation, and voice acknowledgment, and so forth. One of the basic parts achieving these achievements comes about is convolutional neural networks (CNN) [70]. Krizhevsky et al [71] proposed a concept called AlexNet after which, CNNs have exhibited prevalent execution for picture classification differentiated and conventional ‘shallow learning’ strategies, and have similarly been adequately associated for object detection [72], video classification [73] and segmentation [24], etc.…”

Section: Vehicle Detectionmentioning

confidence: 99%

Vehicle detection in intelligent transport system under a hazy environment: a survey

Husain

Maity

Yadav³

2020

IET Image Processing

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An Ensemble Deep Learning Method for Vehicle Type Classification on Visual Traffic Surveillance Sensors

Cited by 97 publications

References 25 publications

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

An Integrated Self-Diagnosis System for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Vehicle detection in intelligent transport system under a hazy environment: a survey

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