Design of Visual Detection System for Papaya Surfaces

2011 ◽  
Author(s):  
He Fang ◽  
Zhang Yunwei ◽  
Zhou Xuan
Keyword(s):  
Visual Detection ◽  
Detection System

The Error Analysis about Gauge Visual Detection System between High Signals and Contact Net

2012 ◽  
Vol 522 ◽  
pp. 347-350
Author(s):  
Xi Lin Zhu ◽  
Yong Yu ◽  
Qiang Wei ◽  
Xiang Zou ◽  
Chen Jun Huang
Keyword(s):  
Image Processing ◽  
Error Analysis ◽  
System Design ◽  
Visual Detection ◽  
Imaging System ◽  
Detection System ◽  
Systematic Errors ◽  
Lighting Conditions ◽  
Specific Impact

t is need to experiment to verify the correctness and validity of various stages in system design after gauge visual detection system designing between high signals and contact net. Then it does error analysis from lighting conditions, camera resolution, binocular imaging system installation structure, camera out of synchronized, noise and subsequent image processing operations, etc. It analysis the systematic errors principle and specific impact, then identifies specific improvements.

scholarly journals A machine learning-based system for detecting leishmaniasis in microscopic images

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Mojtaba Zare ◽  
Hossein Akbarialiabad ◽  
Hossein Parsaei ◽  
Qasem Asgari ◽  
Ali Alinejad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Visual Detection ◽  
Detection System ◽  
Cost Effective ◽  
Leishmania Parasite ◽  
Parasitic Disease ◽  
Integral Image ◽  
Parasite Detection ◽  
Image Creation

Abstract Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

A biomimetic cerium-based biosensor for the direct visual detection of phosphate under physiological conditions

2019 ◽  
Author(s):  
Thibaud Rossel ◽  
Marc Creus
Keyword(s):  
Visual Detection ◽  
Detection System ◽  
Physiological Conditions ◽  
Naked Eye ◽  
Neutral Ph ◽  
Phosphate Ions ◽  
Homoleptic Complex ◽  
Displacement Assay

<div><div><div><p> </p><p>An indicator displacement assay (IDA) was used to probe phosphate ions in acqueous medium at neutral pH using a dinuclear cerium based complex [Ce<sub>2</sub>(HXTA)]3+. The homoleptic complex can be used to detect phosphate ions in micromolar concentrations either spectrophotometrically or with the naked-eye. To our knowledge, this is the biomimetic detection system with the highest affinity known to date for selective, naked-eye based phosphate recognition under physiological conditions.</p> </div></div></div>

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.

scholarly journals DESIGN AND IMPLEMENTATION OF THE VISUAL DETECTION SYSTEM FOR AMPHIBIOUS ROBOTS

2019 ◽  
Vol 34 (4) ◽  
Author(s):  
Yanlin He ◽  
Xu Zhang ◽  
Lianqing Zhu ◽  
Guangkai Sun ◽  
Junfei Qiao
Keyword(s):  
Visual Detection ◽  
Detection System ◽  
Design And Implementation

scholarly journals System for an acoustic detection, localisation and classification

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 62
Author(s):  
Jakub Svatos ◽  
Jan Holub ◽  
Jan Belak
Keyword(s):  
Visual Detection ◽  
Detection System ◽  
False Alarms ◽  
Classification Algorithms ◽  
Acoustic Detection ◽  
Detection Techniques ◽  
Acoustic Detector ◽  
Military Applications ◽  
Transformation Methods

<p class="Abstract">Currently, acoustic detection techniques of gunshots (gunshot detection and its classification) are increasingly being used not only for military applications but also for civilian purposes. Detection, localisation, and classification of a dangerous event such as gunshots employing acoustic detection is a perspective alternative to visual detection, which is commonly used. In some situations, to detect and localise the source of a gunshot, an automatic acoustic detection system, which can classify the caliber, may be preferable. This paper presents a system for acoustic detection, which can detect, localise and classify acoustic events such as gunshots. The system has been tested in open and closed shooting ranges and tested firearms are 9 mm short gun, 6.35 mm short gun, .22 short gun, and .22 rifle gun with various subsonic and supersonic ammunition. As ‘false alarms’, sets of different impulse acoustic events like door slams, breaking glass, etc. have been used. Localisation and classification algorithms are also introduced. To successfully classify the tested acoustic signals, Continuous Wavelet and Mel Frequency Transformation methods have been used for the signal processing, and the fully two-layer connected neural network has been implemented. The results show that the acoustic detector can be used for reliable gunshot detection, localisation, and classification.</p>

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