scholarly journals Computer Vision-Based Portable System for Nitroaromatics Discrimination

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Nuria López-Ruiz ◽  
Miguel M. Erenas ◽  
Ignacio de Orbe-Payá ◽  
Luis F. Capitán-Vallvey ◽  
Alberto J. Palma ◽  
...  
Keyword(s):  
Computer Vision ◽  
Picric Acid ◽  
Color Space ◽  
Control Unit ◽  
Nitroaromatic Compounds ◽  
Raspberry Pi ◽  
Real Time Analysis ◽  
Portable System ◽  
Related Compounds ◽  
Explosives And Related Compounds

A computer vision-based portable measurement system is presented in this report. The system is based on a compact reader unit composed of a microcamera and a Raspberry Pi board as control unit. This reader can acquire and process images of a sensor array formed by four nonselective sensing chemistries. Processing these array images it is possible to identify and quantify eight different nitroaromatic compounds (both explosives and related compounds) by using chromatic coordinates of a color space. The system is also capable of sending the obtained information after the processing by a WiFi link to a smartphone in order to present the analysis result to the final user. The identification and quantification algorithm programmed in the Raspberry board is easy and quick enough to allow real time analysis. Nitroaromatic compounds analyzed in the range of mg/L were picric acid, 2,4-dinitrotoluene (2,4-DNT), 1,3-dinitrobenzene (1,3-DNB), 3,5-dinitrobenzonitrile (3,5-DNBN), 2-chloro-3,5-dinitrobenzotrifluoride (2-C-3,5-DNBF), 1,3,5-trinitrobenzene (TNB), 2,4,6-trinitrotoluene (TNT), and tetryl (TT).

scholarly journals CONTOUR METHOD FOR POSITIONING OBJECTS IN MOBILE COMPUTER VISION SYSTEMS

2021 ◽  
pp. 62-69
Author(s):  
Sergey Kondratyev ◽  
Vitaliy Kostenko ◽  
Marina Yadrova
Keyword(s):  
Computer Vision ◽  
Control Unit ◽  
Contour Method ◽  
Raspberry Pi ◽  
Closed Contour ◽  
Vision Systems ◽  
Mobile Computer ◽  
Video Cameras ◽  
Computer Vision Systems

The paper considers the possibility of solving the problem of improving the quality of technical vision using the contour method, which is used to position objects in mobile computer vision systems. The hardware part of the object positioning system includes two video cameras, a Raspberry Pi 3 microcomputer, a depth contour map screen, and a motor control unit. The codes of programs based on the OpenCV library, the algorithm of the system and examples of the implementation of the contour method are given. The algorithm of the developed positioning technique includes the selection of the contours of objects on the frames of a stereopair, removal of all open contours, calculation of the moment (center of mass) of each closed contour, determination of the displacement along the x-axis of the moments of the corresponding contours, filling each closed contour with points with a brightness inversely proportional to the displacement of the moments. The presence of two video cameras, a Raspberry Pi 3 microcomputer, a contour depth map screen provides stereoscopic and panoramic "vision", that is, the ability to determine the presence of objects and their distance, as well as to get an overall picture in the "field of view" of the system. The engine control unit allows mobile devices to avoid obstacles. Based on the analysis of the research results, it was found that the proposed system provides an increase in the quality of positioning of objects and a decrease in the required computing resource, which gives a significant decrease in power consumption and ensures the autonomy of the system.

Developing Algorithms for a Berry Recognition System Used in Robotized Harvesting of Garden Strawberry

2020 ◽  
Vol 67 (1) ◽  
pp. 133-141
Author(s):  
Dmitriy O. Khort ◽  
Aleksei I. Kutyrev ◽  
Igor G. Smirnov ◽  
Rostislav A. Filippov ◽  
Roman V. Vershinin
Keyword(s):  
Computer Vision ◽  
Technological Process ◽  
Degrees Of Freedom ◽  
Computer Control ◽  
Recognition System ◽  
Raspberry Pi ◽  
Laser Sensors ◽  
Lighting Conditions ◽  
Systems Research ◽  
Degree Of Maturity

Technological capabilities of agricultural units cannot be optimally used without extensive automation of production processes and the use of advanced computer control systems. (Research purpose) To develop an algorithm for recognizing the coordinates of the location and ripeness of garden strawberries in different lighting conditions and describe the technological process of its harvesting in field conditions using a robotic actuator mounted on a self-propelled platform. (Materials and methods) The authors have developed a self-propelled platform with an automatic actuator for harvesting garden strawberry, which includes an actuator with six degrees of freedom, a co-axial gripper, mg966r servos, a PCA9685 controller, a Logitech HD C270 computer vision camera, a single-board Raspberry Pi 3 Model B+ computer, VL53L0X laser sensors, a SZBK07 300W voltage regulator, a Hubsan X4 Pro H109S Li-polymer battery. (Results and discussion) Using the Python programming language 3.7.2, the authors have developed a control algorithm for the automatic actuator, including operations to determine the X and Y coordinates of berries, their degree of maturity, as well as to calculate the distance to berries. It has been found that the effectiveness of detecting berries, their area and boundaries with a camera and the OpenCV library at the illumination of 300 Lux reaches 94.6 percent’s. With an increase in the robotic platform speed to 1.5 kilometre per hour and at the illumination of 300 Lux, the average area of the recognized berries decreased by 9 percent’s to 95.1 square centimeter, at the illumination of 200 Lux, the area of recognized berries decreased by 17.8 percent’s to 88 square centimeter, and at the illumination of 100 Lux, the area of recognized berries decreased by 36.4 percent’s to 76 square centimeter as compared to the real area of berries. (Conclusions) The authors have provided rationale for the technological process and developed an algorithm for harvesting garden strawberry using a robotic actuator mounted on a self-propelled platform. It has been proved that lighting conditions have a significant impact on the determination of the area, boundaries and ripeness of berries using a computer vision camera.

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

scholarly journals UAV Landing Using Computer Vision Techniques for Human Detection

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 613
Author(s):  
David Safadinho ◽  
João Ramos ◽  
Roberto Ribeiro ◽  
Vítor Filipe ◽  
João Barroso ◽  
...  
Keyword(s):  
Computer Vision ◽  
Tall Buildings ◽  
Landing Site ◽  
Cost Effective ◽  
Human Detection ◽  
Raspberry Pi ◽  
Single Shot ◽  
Irregular Terrain ◽  
Effective System ◽  
Satellite Signal

The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed—without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5–10 m, with recalls from 59%–76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.

Microbial Degradation of Explosives and Related Compounds

1994 ◽  
Vol 20 (4) ◽  
pp. 265-284 ◽  
Author(s):  
T. Gorontzy ◽  
O. Drzyzga ◽  
M. W. Kahl ◽  
D. Bruns-nagel ◽  
J. Breitung ◽  
...  
Keyword(s):  
Microbial Degradation ◽  
Related Compounds ◽  
Explosives And Related Compounds
Sign in / Sign up

Export Citation Format

Share Document