scholarly journals The visual detection of driving while intoxicated: Field test of visual cues and detection methods

1980 ◽  
Author(s):  
Douglas H. Harris ◽  
Robert A. Dick ◽  
Steven M. Casey ◽  
Christopher J. Jarosz
Keyword(s):  
Field Test ◽  
Visual Cues ◽  
Visual Detection ◽  
Detection Methods ◽  
Driving While Intoxicated

On-the-Road Detection of Driving While Intoxicated

1979 ◽  
Vol 23 (1) ◽  
pp. 263-266
Author(s):  
Douglas H. Harris
Keyword(s):  
Law Enforcement ◽  
Police Officers ◽  
Visual Cues ◽  
Frequency Of Occurrence ◽  
Road Detection ◽  
Patrol Officers ◽  
Law Enforcement Agencies ◽  
Driving While Intoxicated ◽  
The Road ◽  
On The Road

Visual cues were identified and procedures were developed to enhance on-the-road detection of driving while intoxicated (DWI) by police patrol officers. Related research was reviewed; police officers with demonstrated effectiveness in DWI detection were interviewed; DWI arrest reports were analyzed; and a study was conducted to determine the frequency of occurrence and relative discriminability of potential visual cues. Based on the results, a DWI detection Guide was developed; the Guide is currently being verified and evaluated in a field-study involving a sample of 10 law enforcement agencies.

scholarly journals Making the Invisible Visible: Verbal but Not Visual Cues Enhance Visual Detection

PLoS ONE ◽  
2010 ◽  
Vol 5 (7) ◽  
pp. e11452 ◽  
Author(s):  
Gary Lupyan ◽  
Michael J. Spivey
Keyword(s):  
Visual Cues ◽  
Visual Detection ◽  
The Invisible

scholarly journals Evaluation of CAMP-Like Effect, Biofilm Formation, and Discrimination ofCandida africanafrom VaginalCandida albicansSpecies

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Keyvan Pakshir ◽  
Mahboubeh Bordbar ◽  
Kamiar Zomorodian ◽  
Hasti Nouraei ◽  
Hossein Khodadadi
Keyword(s):  
Staphylococcus Epidermidis ◽  
Biofilm Formation ◽  
Visual Detection ◽  
Bacterial Flora ◽  
Detection Methods ◽  
Vaginal Candidiasis ◽  
Molecular Assay ◽  
Agar Media ◽  
Candida Africana ◽  
Female Genital

Candida africana asa species recovered from female genital specimens is highly close toC. albicans. The present study was conducted to discriminateC. africanafrom presumptive vaginalC. albicansstrains by molecular assay and evaluate their hemolysin activity, biofilm formation, and cohemolytic effect (CAMP) with vaginal bacterial flora. A total of 110 stock vaginalC. albicansisolates were examined byHWP1gene amplification. Hemolysin activity and the ability of biofilm formation were evaluated by blood plate assay and visual detection methods, respectively.Staphylococcus aureus,Staphylococcus epidermidis, andStreptococcus agalactiaewere used to evaluate the CAMP-like effects in Sabouraud blood agar media. Based on the size of the amplicons (941 bp), all isolates were identified asC. albicans. All samples were able to produce beta-hemolysin. Moreover, 69 out of 110 of the isolates (62.7%) were biofilm-positive, 54 out of 110Candidaisolates (49%) demonstrated cohemolytic effects withS. agalactiae, and 48 out of 110 showed this effect withS. aureus(43.6%). All isolates were CAMP-negative withS. epidermidis. We detected all isolates asCandida albicansand almost half of the isolates were CAMP-positive withS. aureusandS. agalactiae, suggesting that these bacteria increase the pathogenicity ofCandidain vaginal candidiasis.

The Method of Recognizing Navigation Lights in the Runway Debris Visual Automatic Detection System

2011 ◽  
Vol 121-126 ◽  
pp. 2333-2337
Author(s):  
Zhi Jing Yu ◽  
Feng Ze Lang ◽  
Xiao Jing Guo
Keyword(s):  
Visual Detection ◽  
Detection System ◽  
Estimation Method ◽  
Automatic Detection ◽  
Detection Methods ◽  
Experimental Result ◽  
Scanning System ◽  
Result Show ◽  
Automatic Detection System ◽  
System Orientation

Runway debris visual automatic detection system is one of key measures that ensure efficient and safe airport operation. It can automatically recognize runway debris with visual detection methods, and determine the orientation of scanning system with attitude estimation method, then realize automatically detect debris. In this paper, the recognizing method of fixed feature navigation lights that are used to calculate system orientation are researched on. Runway area and background are detached by recognizing runway lines. Navigation lights are recognized and matched by the method of image matching. Experimental result show, runway can be separated and recognized from complex environment, and navigational lights can be matched quickly in the algorithm. The reliable fixed feature is supplied for followed determining scanning system orientation and located debris.

Detection of Component Types and Track Damage for High-Speed Railway Using Region-Based Convolutional Neural Networks

2018 ◽  
Author(s):  
Shengyuan Li ◽  
Peigang Li ◽  
Yang Zhang ◽  
Xuefeng Zhao
Keyword(s):  
High Speed ◽  
Visual Inspection ◽  
Visual Detection ◽  
Detection Method ◽  
Detection Methods ◽  
Image Process ◽  
Training Process ◽  
High Speed Railway ◽  
Subjective Judgement ◽  
Damage Type

High-speed railway plays critical roles in public safety and the country’s economy. Visual detection of components and damages can reflect the health conditions of high-speed railway. Human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. Image-based detection methods abandon the weakness of human-based visual inspection. However, in practice, the complex real-world situations, such as lighting and shadow changes, can lead to challenges to the wide adaptability of image process techniques. To overcome these challenges, this paper provides a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based detection method of component types and track damage for high-speed railway. To realize the method, a database including 575 images labeled for three component types and one track damage type of high-speed railway is built. A Faster R-CNN architecture based on ZF-Net is modified, then trained and validated using the built database. The performance of the trained Faster R-CNN is evaluated using 50 new images which are not be used for training process. The results show that the proposed method can indeed detect the component types and track damage for high-speed railway.

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