Automated Vehicle Classification System for Austroads Standard Based Upon Laser Sensor Technology

2009 ◽  
Vol 5 (2) ◽  
pp. 95-106
Author(s):  
W. Xiang ◽  
C. Otto ◽  
P. Wen
Keyword(s):  
Classification System ◽  
Vehicle Classification ◽  
Sensor Technology ◽  
Laser Sensor ◽  
Automated Vehicle

A Differential Evolution Based Multiclass Vehicle Detector and Classifier for Urban Environments

2017 ◽  
Vol 8 (3) ◽  
pp. 19-42 ◽  
Author(s):  
Deepak Dawar ◽  
Simone A. Ludwig
Keyword(s):  
Differential Evolution ◽  
Intelligent Transportation Systems ◽  
Classification System ◽  
Flow Analysis ◽  
Transportation Systems ◽  
Urban Environments ◽  
Vehicle Classification ◽  
Discrete Parameter ◽  
On The Road ◽  
Transportation Agencies

Video analytics is emerging as a high potential area supplementing intelligent transportation systems (ITSs) with wide ranging applications from traffic flow analysis to surveillance. Object detection and classification, as a sub part of a video analytical system, could potentially help transportation agencies to analyze and respond to traffic incidents in real time, plan for possible future cascading events, or use the classification data to design better roads. This work presents a specialized vehicle classification system for urban environments. The system is targeted at the analysis of vehicles, especially trucks, in urban two lane traffic, to empower local transportation agencies to decide on the road width and thickness. The main thrust is on the accurate classification of the vehicles detected using an evolutionary algorithm. The detector is backed by a differential evolution (DE) based discrete parameter optimizer. The authors show that, though employing DE proves expensive in terms of computational cycles, it measurably improves the accuracy of the classification system.

scholarly journals A Lightweight Moving Vehicle Classification System Through Attention-Based Method and Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 157564-157573
Author(s):  
Nasaruddin Nasaruddin ◽  
Kahlil Muchtar ◽  
Afdhal Afdhal
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
Vehicle Classification ◽  
Moving Vehicle

Vehicle Classification System Using In-Pavement Fiber Bragg Grating Sensors

2018 ◽  
Vol 18 (7) ◽  
pp. 2807-2815 ◽  
Author(s):  
Mu'ath Al-Tarawneh ◽  
Ying Huang ◽  
Pan Lu ◽  
Denver Tolliver
Keyword(s):  
Fiber Bragg Grating ◽  
Classification System ◽  
Vehicle Classification ◽  
Bragg Grating ◽  
Fiber Bragg Grating Sensors

scholarly journals Laser Sensors for Displacement, Distance and Position

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1924 ◽  
Author(s):  
Young Soo Suh
Keyword(s):  
Manufacturing Industry ◽  
Time Of Flight ◽  
Autonomous Driving ◽  
Sensor Technology ◽  
Laser Sensor ◽  
Laser Sensors ◽  
Interferometric Sensors ◽  
Industry Applications ◽  
Surface Profiles ◽  
Traditional Manufacturing

Laser sensors can be used to measure distances to objects and their related parameters (displacements, position, surface profiles and velocities). Laser sensors are based on many different optical techniques, such as triangulation, time-of-flight, confocal and interferometric sensors. As laser sensor technology has improved, the size and cost of sensors have decreased, which has led to the widespread use of laser sensors in many areas. In addition to traditional manufacturing industry applications, laser sensors are increasingly used in robotics, surveillance, autonomous driving and biomedical areas. This paper outlines some of the recent efforts made towards laser sensors for displacement, distance and position.

Acceleration-Based Automated Vehicle Classification on Mobile Bridges

2016 ◽  
Vol 31 (11) ◽  
pp. 813-825 ◽  
Author(s):  
Chul Min Yeum ◽  
Shirley J. Dyke ◽  
Ricardo E. Basora Rovira ◽  
Christian Silva ◽  
Jeff Demo
Keyword(s):  
Vehicle Classification ◽  
Automated Vehicle

scholarly journals A Highly Selective Vehicle Classification Utilizing Dual-Loop Inductive Detector

2014 ◽  
Vol 21 (3) ◽  
pp. 473-484 ◽  
Author(s):  
Janusz Gajda ◽  
Marek Stencel
Keyword(s):  
Classification System ◽  
Vehicle Classification ◽  
Classification Algorithms ◽  
Characteristic Parameters ◽  
Independent Measurement ◽  
Reactive Component ◽  
New Approach ◽  
Inductive Loop ◽  
Magnetic Signature ◽  
Source Of Information

Abstract In general, currently employed vehicle classification algorithms based on the magnetic signature can distinguish among only a few vehicle classes. The work presents a new approach to this problem. A set of characteristic parameters measurable from the magnetic signature and limits of their uncertainty intervals are determined independently for each predefined class. The source of information on the vehicle parameters is its magnetic signature measured in a system that enables independent measurement of two signals, i.e. changes in the active and reactive component of the inductive loop impedance caused by a passing vehicle. These innovations result in high selective classification system, which utilizes over a dozen vehicle classes. The evaluation of the proposed approach was carried out for good vehicles consisting of 2-axle tractor and a 3-axle semi-trailer.

scholarly journals Use of a 3D model to improve the performance of laser-based railway track inspection

2018 ◽  
Vol 233 (3) ◽  
pp. 337-355 ◽  
Author(s):  
Jiaqi Ye ◽  
Edward Stewart ◽  
Clive Roberts
Keyword(s):  
3D Reconstruction ◽  
3D Model ◽  
Surface Defects ◽  
Railway Track ◽  
Sensor Technology ◽  
Prototype System ◽  
Laser Sensor ◽  
Railway Systems ◽  
3D Data Processing ◽  
Inspection Techniques

In recent decades, 3D reconstruction techniques have been applied in an increasing number of areas such as virtual reality, robot navigation, medical imaging and architectural restoration of cultural relics. Most of the inspection techniques used in railway systems are, however, still implemented on a 2D basis. This is particularly true of track inspection due to its linear nature. Benefiting from the development of sensor technology and constantly improving processors, higher quality 3D model reconstructions are becoming possible which push the technology into more challenging areas. One such advancement is the use of 3D perceptual techniques in railway systems. This paper presents a novel 3D perceptual system, based on a low-cost 2D laser sensor, which has been developed for the detection and characterisation of physical surface defects in railway tracks. An innovative prototype system has been developed to capture and correlate the laser scan data; dedicated 3D data processing procedures have then been developed in the form of three specific defect-detection algorithms (depth gradient, face normal and face-normal gradient) which are applied to the 3D model. The system has been tested with rail samples in the laboratory and at the Long Marston Railway Test Track. The 3D models developed represent the external surface of the samples both laterally (2D slices) and longitudinally (3D model), and common surface defects can be detected and represented in 3D. The results demonstrate the feasibility of applying 3D reconstruction-based inspection techniques to railway systems.

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