2,215 research outputs found
Comparing the Performance of Deep Learning Algorithms for Vehicle Detection and Classification
The rapid pace of developments in Artificial Intelligence (AI) provides unprecedented opportunities to enhance the performance of Intelligent Transportation Systems. Automating vehicle detection and classification using computer vision methods can complement traditional sensors or serve as a cost-effective and environmentally friendly substitute for conventional sensors. This study investigates the robustness of existing deep learning models for vehicle identification and classification using a heterogenous dataset. The dataset is grouped into six distinct classes based on the Federal Highway Administration (FHWA) vehicle classification scheme. This study uses three different versions of You Only Look Once (YOLO) single-stage object detection models, namely YOLOv7, YOLOv5m, and YOLOv5s. The comparative evaluation will depend on four performance metrics: recall, precision, F1-score and mean average precision (MAP). The results show that for this case study, YOLOv7 outperformed the other models with 84.7% precision, 89.4% recall, 86.1% F1-score and 93% MAP at 0.5, and 82.4% MAP at 0.95
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey
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.Comment: Published in IEEE Acces
Truck Trailer Classification Using Side-Fire Light Detection And Ranging (LiDAR) Data
Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources.
Nevertheless, using current emerging technologies such as Light Detection and Ranging (LiDAR) data, it may be possible to predict commodity type from truck body types or trailers. For example, refrigerated trailers are commonly used to transport perishable produce and meat products, tank trailers are for fuel and other liquid products, and specialized trailers carry livestock. The main goal of this research is to develop methods using side-fired LiDAR data to distinguish between specific types of truck trailers beyond what is generally possible with traditional vehicle classification sensors (e.g., piezoelectric sensors and inductive loop detectors).
A multi-array LiDAR sensor enables the construction of 3D-profiles of vehicles since it measures the distance to the object reflecting its emitted light. In this research 16-beam LiDAR sensor data are processed to estimate vehicle speed and extract useful information and features to classify semi-trailer trucks hauling ten different types of trailers: a reefer and non-reefer dry van, 20 ft and 40 ft intermodal containers, a 40 ft reefer intermodal container, platforms, tanks, car transporters, open-top van/dump and aggregated other types (i.e., livestock, logging, etc.). In addition to truck-trailer classification, methods are developed to detect empty and loaded platform semi-trailers. K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), and Support Vector Machines (SVM) supervised machine learning algorithms are implemented on the field data collected on a freeway segment that includes over seven-thousand trucks. The results show that different trailer body types and empty and loaded platform semi-trailers can be classified with a very high level of accuracy ranging from 85% to 98% and 99%, respectively. To enhance the accuracy by which multiple LiDAR frames belonging to the same truck are merged, a new algorithm is developed to estimate the speed while the truck is within the field of view of the sensor. This algorithm is based on tracking tires and utilizes line detection concepts from image processing. The proposed algorithm improves the results and allows creating more accurate 2D and 3D truck profiles as documented in this thesis
Scene Detection Classification and Tracking for Self-Driven Vehicle
A number of traffic-related issues, including crashes, jams, and pollution, could be resolved by self-driving vehicles (SDVs). Several challenges still need to be overcome, particularly in the areas of precise environmental perception, observed detection, and its classification, to allow the safe navigation of autonomous vehicles (AVs) in crowded urban situations. This article offers a comprehensive examination of the application of deep learning techniques in self-driving cars for scene perception and observed detection. The theoretical foundations of self-driving cars are examined in depth in this research using a deep learning methodology. It explores the current applications of deep learning in this area and provides critical evaluations of their efficacy. This essay begins with an introduction to the ideas of computer vision, deep learning, and self-driving automobiles. It also gives a brief review of artificial general intelligence, highlighting its applicability to the subject at hand. The paper then concentrates on categorising current, robust deep learning libraries and considers their critical contribution to the development of deep learning techniques. The dataset used as label for scene detection for self-driven vehicle. The discussion of several strategies that explicitly handle picture perception issues faced in real-time driving scenarios takes up a sizeable amount of the work. These methods include methods for item detection, recognition, and scene comprehension. In this study, self-driving automobile implementations and tests are critically assessed
Mapping innovation in the European transport sector : An assessment of R&D efforts and priorities, institutional capacities, drivers and barriers to innovation
The present document provides an overview of the innovation capacity of the European transport sectors. The analysis addresses transport-related innovation from three different angles. It identifies the drivers and barriers to innovation for the main transport sub-sectors; it assesses quantitative indicators through the detailed analysis of the main industrial R&D investors and public R&D priorities in transport; and it identifies the key actors for transport research and knowledge flows between them in order to detect shortcomings in the current institutional set-up of transport innovation. The analysis finds that despite the significant on-going research efforts in transport, largely driven by the automotive industry, the potential for systemic innovations that go beyond modal boundaries and leave the currently pre-dominant design are under-exploited due to prominent lock-in effects caused by infrastructure and the institutional set-up of the innovation systemsJRC.J.1-Economics of Climate Change, Energy and Transpor
Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras
Traffic cameras remain the primary source data for surveillance activities
such as congestion and incident monitoring. To date, State agencies continue to
rely on manual effort to extract data from networked cameras due to limitations
of the current automatic vision systems including requirements for complex
camera calibration and inability to generate high resolution data. This study
implements a three-stage video analytics framework for extracting
high-resolution traffic data such vehicle counts, speed, and acceleration from
infrastructure-mounted CCTV cameras. The key components of the framework
include object recognition, perspective transformation, and vehicle trajectory
reconstruction for traffic data collection. First, a state-of-the-art vehicle
recognition model is implemented to detect and classify vehicles. Next, to
correct for camera distortion and reduce partial occlusion, an algorithm
inspired by two-point linear perspective is utilized to extracts the region of
interest (ROI) automatically, while a 2D homography technique transforms the
CCTV view to bird's-eye view (BEV). Cameras are calibrated with a two-layer
matrix system to enable the extraction of speed and acceleration by converting
image coordinates to real-world measurements. Individual vehicle trajectories
are constructed and compared in BEV using two time-space-feature-based object
trackers, namely Motpy and BYTETrack. The results of the current study showed
about +/- 4.5% error rate for directional traffic counts, less than 10% MSE for
speed bias between camera estimates in comparison to estimates from probe data
sources. Extracting high-resolution data from traffic cameras has several
implications, ranging from improvements in traffic management and identify
dangerous driving behavior, high-risk areas for accidents, and other safety
concerns, enabling proactive measures to reduce accidents and fatalities.Comment: 25 pages, 9 figures, this paper was submitted for consideration for
presentation at the 102nd Annual Meeting of the Transportation Research
Board, January 202
Investigation of Truck Data Collection using LiDAR Sensing Technology Along Rural Highways
65A0674 TO 036LiDAR is an emerging technology that can provide detailed point-cloud measurements of objects. The purpose of this study is to investigate the use of LiDAR technology for accurate classification of trucks according to the established FHWA scheme along rural highway corridors as an alternative to in-pavement detector infrastructure - such as inductive loop sensors and piezo-based automatic vehicle classifiers which is not widely deployed along many rural highway corridors - and temporary sensors such as pneumatic road tubes, which expose workers to live traffic. This research will also investigate anonymous tracking of trucks with LiDAR across two locations using advanced algorithms. This can be used to measure spatial activity and travel time performance of trucks along instrumented corridors
- …