scholarly journals Vehicle Identification using Optimized ALPR

2021 ◽  
Author(s):  
Najmath Ottakath ◽  
Abdulla Al-Ali ◽  
Somaya Al Maadeed
Keyword(s):  
Object Detection ◽  
Parameter Tuning ◽  
Mean Average Precision ◽  
License Plate ◽  
Average Precision ◽  
The Road ◽  
Detection Model ◽  
Art Object ◽  
On The Road ◽  
The Right

Vehicles are a common sight on the road. Tracking and monitoring suspicious vehicles for identification due to high similarity in structure and form leads to difficulties in differentiating between them. The unique identity of a vehicle, the license plate is used here for this purpose. License plate detection is considered as an object detection task. Transfer learning on pre-trained state of art object detection models is an approach, which can perform this with better accuracy in terms of mean average precision. However, setting the right hyper-parameters needs multiple experiments. In this research, an evolutionary algorithm, genetic algorithm is used, which can optimize the hyper-parameters to achieve the best accuracy for the object detection model, YOLOv5. Further, the license plate was identified using OCR. This study concluded that hyper-parameter tuning achieved high accuracy in terms of mean average precision, achieving 98.25%, compared to 80% in initial parameter set providing an automated optimization. This license plate detected can be stored in a secure location and retrieved for re-identification. A decentralized storage or a secure cloud can be used to store the license plate. The application of this is most relevant to surveillance in high security locations where suspicious vehicles must be tracked.

scholarly journals Improve YOLOv3 using dilated spatial pyramid module for multi-scale object detection

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142093606
Author(s):  
Xiaoguo Zhang ◽  
Ye Gao ◽  
Huiqing Wang ◽  
Qing Wang
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Receptive Fields ◽  
Mean Average Precision ◽  
Feature Maps ◽  
Average Precision ◽  
Multi Scale ◽  
Art Object ◽  
Spatial Pyramid ◽  
Scale Variation

Effectively and efficiently recognizing multi-scale objects is one of the key challenges of utilizing deep convolutional neural network to the object detection field. YOLOv3 (You only look once v3) is the state-of-the-art object detector with good performance in both aspects of accuracy and speed; however, the scale variation is still the challenging problem which needs to be improved. Considering that the detection performances of multi-scale objects are related to the receptive fields of the network, in this work, we propose a novel dilated spatial pyramid module to integrate multi-scale information to effectively deal with scale variation problem. Firstly, the input of dilated spatial pyramid is fed into multiple parallel branches with different dilation rates to generate feature maps with different receptive fields. Then, the input of dilated spatial pyramid and outputs of different branches are concatenated to integrate multi-scale information. Moreover, dilated spatial pyramid is integrated with YOLOv3 in front of the first detection header to present dilated spatial pyramid-You only look once model. Experiment results on PASCAL VOC2007 demonstrate that dilated spatial pyramid-You only look once model outperforms other state-of-the-art methods in mean average precision, while it still keeps a satisfying real-time detection speed. For 416 × 416 input, dilated spatial pyramid-You only look once model achieves 82.2% mean average precision at 56 frames per second, 3.9% higher than YOLOv3 with only slight speed drops.

scholarly journals Automatic Roadway Features Detection with Oriented Object Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3531
Author(s):  
Hesham M. Eraqi ◽  
Karim Soliman ◽  
Dalia Said ◽  
Omar R. Elezaby ◽  
Mohamed N. Moustafa ◽  
...  
Keyword(s):  
Object Detection ◽  
Safety Evaluation ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
The Road ◽  
Detection Model ◽  
Detection Approach ◽  
Roadway Safety ◽  
Safety Features ◽  
Oriented Object

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

Determination of Resistance to Abrasive Wear. VIII. Influence of Softeners in Tests with the Akron-Croydon and Du Pont Machines

1949 ◽  
Vol 22 (1) ◽  
pp. 259-262
Author(s):  
J. F. Morley
Keyword(s):  
Unit Area ◽  
Published Data ◽  
The Road ◽  
Abrasive Surface ◽  
Abrasive Power ◽  
The Difference ◽  
On The Road ◽  
The Right ◽  
Abrasion Testing

Abstract These experiments indicate that softeners can influence abrasion resistance, as measured by laboratory machines, in some manner other than by altering the stress-strain properties of the rubber. One possible explanation is that the softener acts as a lubricant to the abrasive surface. Since this surface, in laboratory abrasion-testing machines, is relatively small, and comes repeatedly into contact with the rubber under test, it seems possible that it may become coated with a thin layer of softener that reduces its abrasive power. It would be interesting in this connection to try an abrasive machine in which a long continuous strip of abrasive material was used, no part of it being used more than once, so as to eliminate or minimize this lubricating effect. The fact that the effect of the softener is more pronounced on the du Pont than on the Akron-Croydon machine lends support to the lubrication hypothesis, because on the former machine the rate of wear per unit area of abrasive is much greater. Thus in the present tests the volume of rubber abraded per hr. per sq. cm. of abrasive surface ranges from 0.03 to 0.11 cc. on the du Pont machine and from 0.0035 to 0.0045 cc. on the Akron-Croydon machine. On the other hand, if the softener acts as a lubricant, it would be expected to reduce considerably the friction between the abrasive and the rubber and hence the energy used in dragging the rubber over the abrasive surface. The energy figures given in the right-hand columns of Tables 1 and 3, however, show that there is relatively little variation between the different rubbers. As a test of the lubrication hypothesis, it would be of interest to vary the conditions of test so that approximately the same amount of rubber per unit area of abrasive is abraded in a given time on both machines; this should show whether the phenomena observed under the present test conditions are due solely to the difference in rate of wear or to an inherent difference in the type of wear on the two machines. This could most conveniently be done by considerably reducing the load on the du Pont machine. In the original work on this machine the load was standardized at 8 pounds, but no figures are quoted to show how abrasion loss varies with the load. As an addition to the present investigation, it is proposed to examine the effect of this variation with special reference to rubbers containing various amounts and types of softener. Published data on the influence of softeners on the road wear of tire rubbers do not indicate anything like such large effects as are shown by the du Pont machine. This throws some doubt on the value of this machine for testing tire tread rubbers, a conclusion which is confirmed by information obtained from other workers.

scholarly journals Characterisation of cyclists’ willingness to pay for green initiatives at Africa’s largest cycle tour

2016 ◽  
Vol 19 (3) ◽  
pp. 432-439
Author(s):  
Melville Saayman ◽  
Waldo Krugell ◽  
Andrea Saayman
Keyword(s):  
Willingness To Pay ◽  
Carbon Footprint ◽  
Climate Change Mitigation ◽  
Demographic Variables ◽  
The Road ◽  
Road Cycling ◽  
Different Types ◽  
On The Road ◽  
The Right ◽  
Change Mitigation

The Cape Argus Pick n Pay Cycle Tour is a major event on the road cycling calendar. The majority of cyclists travel significant distances and participation produces a substantial carbon footprint. This paper examines participants’ willingness to pay to offset their carbon footprint. The purpose of this paper is to make a contribution to the literature by linking willingness to pay to attitudes towards or beliefs (green views) about the initiatives in place, to ensure a greener cycle tour. Factor analysis is used to identify different types of cyclists, based on their green views: those with green money, those who prefer green products and the “re-cyclers”. The results of the regression analysis reveal that socio-demographic variables and the right attitude towards the environment are significant predictors of stated willingness to pay for climate change mitigation.

scholarly journals Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

The New Conservatives

2017 ◽  
pp. 106-139
Author(s):  
Peter Kolozi
Keyword(s):  
World War Ii ◽  
Cold War ◽  
The State ◽  
Economic Systems ◽  
World War ◽  
Post World War Ii ◽  
The Road ◽  
On The Road ◽  
The Right

Post World War II conservative thinking witnessed a marked shift in criticism away from capitalism itself and to the state. Cold War conservatives’ anti-communism led many on the right to perceive economic systems in stark terms as either purely capitalistic or on the road to communism.

Targeting cardiovascular inflammation: next steps in clinical translation

2020 ◽  
Author(s):  
Patrick R Lawler ◽  
Deepak L Bhatt ◽  
Lucas C Godoy ◽  
Thomas F Lüscher ◽  
Robert O Bonow ◽  
...  
Keyword(s):  
Vascular Inflammation ◽  
Large Body ◽  
High Sensitivity ◽  
Clinical Translation ◽  
Stable Phase ◽  
Atherosclerotic Cardiovascular Disease ◽  
The Road ◽  
Reactive Protein ◽  
On The Road ◽  
The Right

Abstract Systemic vascular inflammation plays multiple maladaptive roles which contribute to the progression and destabilization of atherosclerotic cardiovascular disease (ASCVD). These roles include: (i) driving atheroprogression in the clinically stable phase of disease; (ii) inciting atheroma destabilization and precipitating acute coronary syndromes (ACS); and (iii) responding to cardiomyocyte necrosis in myocardial infarction (MI). Despite an evolving understanding of these biologic processes, successful clinical translation into effective therapies has proven challenging. Realizing the promise of targeting inflammation in the prevention and treatment of ASCVD will likely require more individualized approaches, as the degree of inflammation differs among cardiovascular patients. A large body of evidence has accumulated supporting the use of high-sensitivity C-reactive protein (hsCRP) as a clinical measure of inflammation. Appreciating the mechanistic diversity of ACS triggers and the kinetics of hsCRP in MI may resolve purported inconsistencies from prior observational studies. Future clinical trial designs incorporating hsCRP may hold promise to enable individualized approaches. The aim of this Clinical Review is to summarize the current understanding of how inflammation contributes to ASCVD progression, destabilization, and adverse clinical outcomes. We offer forward-looking perspective on what next steps may enable successful clinical translation into effective therapeutic approaches—enabling targeting the right patients with the right therapy at the right time—on the road to more individualized ASCVD care.

scholarly journals Vehicle Detection and Ranging Using Two Different Focal Length Cameras

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jun Liu ◽  
Rui Zhang
Keyword(s):  
Real Time ◽  
Focal Length ◽  
Vehicle Detection ◽  
Autonomous Driving ◽  
License Plate ◽  
The Road ◽  
Detection Model ◽  
Automatic Driving ◽  
Convolution Structure ◽  
Model Size

Vehicle detection is a crucial task for autonomous driving and demands high accuracy and real-time speed. Considering that the current deep learning object detection model size is too large to be deployed on the vehicle, this paper introduces the lightweight network to modify the feature extraction layer of YOLOv3 and improve the remaining convolution structure, and the improved Lightweight YOLO network reduces the number of network parameters to a quarter. Then, the license plate is detected to calculate the actual vehicle width and the distance between the vehicles is estimated by the width. This paper proposes a detection and ranging fusion method based on two different focal length cameras to solve the problem of difficult detection and low accuracy caused by a small license plate when the distance is far away. The experimental results show that the average precision and recall of the Lightweight YOLO trained on the self-built dataset is 4.43% and 3.54% lower than YOLOv3, respectively, but the computing speed of the network decreases 49 ms per frame. The road experiments in different scenes also show that the long and short focal length camera fusion ranging method dramatically improves the accuracy and stability of ranging. The mean error of ranging results is less than 4%, and the range of stable ranging can reach 100 m. The proposed method can realize real-time vehicle detection and ranging on the on-board embedded platform Jetson Xavier, which satisfies the requirements of automatic driving environment perception.

A Tryst with Reality: Child Labor in the Heart of India, New Delhi

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Rewa Singh
Keyword(s):  
Child Labor ◽  
Life Histories ◽  
Ground Level ◽  
New Delhi ◽  
Social Evil ◽  
The Road ◽  
The People ◽  
The Government ◽  
On The Road ◽  
The Right

“Why do we have to pay the price of poverty? We didn’t create poverty, adults did.” This might be the sentiment of every child who is forced to work at an age when he or she deserves to go to school unlike the fellow kids who are born in a family that can afford to give them a decent childhood. Child Labor is the single most damaging impediment on the road to achieving the goal of development and the purpose of this paper is to show the obstacles that this social evil poses in the path to development. The study used Exploratory, rather unstructured research design and instruments such as case studies and life histories. The study indicates that the government of India has taken some strict measures to eradicate this evil such as the passing of the Right to Education Bill, illegalization of employment of children under the age of 14 years, schemes like “Sarva Siksha Abhiyan” (Education for all campaign), free afternoon meal and so on. But on the ground level their implementation is shoddy due to (as bureaucrats would put it) practical problems. The problem is of course, in the system but it has more to do with the mindsets of the people too. There are people who speak against child labor in India and back at their own house or office, many of them will have at least one child working for them. People need to realize that what a waste of talent and a major obstacle to a country’s development, Child Labor is.   Keywords - Children. Child labor India. Social evil. Illegal employment.

The Real World: Rethinking Immigration

1999 ◽  
Vol 21 (2) ◽  
pp. 53-54 ◽  
Author(s):  
Rob Winthrop
Keyword(s):  
Public Policy ◽  
Task Force ◽  
Policy Issues ◽  
The Road ◽  
Noam Chomsky ◽  
Policy Debates ◽  
National Media ◽  
On The Road ◽  
The One ◽  
The Right

Many of us might aspire to become "public intellectuals," standing side-by-side with Noam Chomsky (for those on the left) or Bill Bennett (for those on the right), using the national media to scourge the politicians, guide the journalists, and correct the wayward public. Unfortunately, few are willing to do the requisite heavy lifting, mastering the details of particular policy debates and cultivating contacts with the relevant players, as first steps on the road to this intellectual Valhalla. As the American Anthropological Association's Task Force on Public Policy commented in its January 1998 report: "Cultural ambivalence within AAA is demonstrated in anthropologists' failure to engage in public policy issues on the one hand, and, on the other hand, anthropologists' indignation at not being consulted on policy issues perceived as being related to anthropology."

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