Fast Automatic Target Detection System Based on a Visible Image Sensor and Ripple Algorithm

2013 ◽  
Vol 13 (7) ◽  
pp. 2720-2728 ◽  
Author(s):  
Jinhui Lan ◽  
Jian Li ◽  
Yong Xiang ◽  
Tonghuan Huang ◽  
Yixin Yin ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection System ◽  
Image Sensor ◽  
Visible Image ◽  
Automatic Target Detection

A Stacked CMOS Active Pixel Image Sensor for Charge Particle Detection and the Application to SIMS

1999 ◽  
Vol 5 (S2) ◽  
pp. 370-371
Author(s):  
H. Yurimoto ◽  
K. Nagashima ◽  
T. Kunihiro ◽  
I. Takayanagi ◽  
J. Nakamura ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Detection System ◽  
Image Sensor ◽  
Two Dimensional ◽  
Particle Detection ◽  
Visible Image ◽  
Two Dimensional Image ◽  
Active Pixel ◽  
Channel Plate ◽  
Pixel Image

A stacked CMOS active pixel image-sensor (APS) has been developed for detecting various kinds of charged particles and its noise performance has been measured and analyzed. The sensitivity for ions and electrons with keV energy level utilizes for ion microscopy such-as SIMS and electron microscopy, respectively.Charge particles such as ions and electrons with kinetic energy of keV order are useful probes for surface analysis of material. A measurement system which yields two-dimensional image of charge particles is highly demanded. The conventional two-dimensional detection system is composed of a micro channel plate, a florescent plate which receives multiplied secondary electrons and generates a visible image, and a visible image sensor. However, its limited dynamic range and non-linearity in the ion-electron-to-photon conversion process make a quantitative measurement difficult. The proposed system using a stacked CMOS APS has several advantages over the conventional system such as high spatial resolution, no insensitive time, high S/N, wide dynamic range, nondestructive readout capability, high robustness, and low power consumption.

Target Detection and Identification Performance Using an Automatic Target Detection System

2016 ◽  
Vol 59 (2) ◽  
pp. 242-258 ◽  
Author(s):  
Adam J. Reiner ◽  
Justin G. Hollands ◽  
Greg A. Jamieson
Keyword(s):  
Target Detection ◽  
Task Difficulty ◽  
Detection System ◽  
Decision Criterion ◽  
Identification Performance ◽  
Automatic Target Detection ◽  
Immersive Virtual Environment ◽  
Detection And Identification ◽  
Scene Illumination ◽  
Night Illumination

Objective: We investigated the effects of automatic target detection (ATD) on the detection and identification performance of soldiers. Background: Prior studies have shown that highlighting targets can aid their detection. We provided soldiers with ATD that was more likely to detect one target identity than another, potentially acting as an implicit identification aid. Method: Twenty-eight soldiers detected and identified simulated human targets in an immersive virtual environment with and without ATD. Task difficulty was manipulated by varying scene illumination (day, night). The ATD identification bias was also manipulated (hostile bias, no bias, and friendly bias). We used signal detection measures to treat the identification results. Results: ATD presence improved detection performance, especially under high task difficulty (night illumination). Identification sensitivity was greater for cued than uncued targets. The identification decision criterion for cued targets varied with the ATD identification bias but showed a “sluggish beta” effect. Conclusion: ATD helps soldiers detect and identify targets. The effects of biased ATD on identification should be considered with respect to the operational context. Application: Less-than-perfectly-reliable ATD is a useful detection aid for dismounted soldiers. Disclosure of known ATD identification bias to the operator may aid the identification process.

FPGA-Based Configurable Frequency-Diverse Ultrasonic Target-Detection System

2011 ◽  
Vol 58 (3) ◽  
pp. 871-879 ◽  
Author(s):  
Joshua Weber ◽  
Erdal Oruklu ◽  
Jafar Saniie
Keyword(s):  
Target Detection ◽  
Detection System

scholarly journals Target Detection System (Tds) for Enhancing Security in Ad Hoc Network

2013 ◽  
Vol 4 (5) ◽  
pp. 15-28
Author(s):  
Hoshiyar Singh Kanyal ◽  
Rahamatkar S ◽  
Sharma B.K ◽  
Bhasker Sharma
Keyword(s):  
Target Detection ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Detection System

scholarly journals Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS)

2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Kisron Kisron ◽  
Bima Sena Bayu Dewantara ◽  
Hary Oktavianto
Keyword(s):  
Operating System ◽  
Computer System ◽  
Target Detection ◽  
Detection System ◽  
Computation Time ◽  
Cascade Algorithm ◽  
Histogram Of Oriented Gradient ◽  
Detection Systems ◽  
Improved Performance ◽  
Improved Accuracy

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.

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