Computer Vision System with Deep Learning for Robotic Arm Control

2018 ◽  
Author(s):  
Rodrigo Melo ◽  
Thelmo Pontes de Araujo ◽  
Arata Andrade Saraiva ◽  
Jose Vigno Moura Sousa ◽  
Nuno Miguel Fonseca Ferreira
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Robotic Arm ◽  
Computer Vision System ◽  
Robotic Arm Control

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals A computer vision system for deep learning-based detection of patient mobilization activities in the ICU

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Serena Yeung ◽  
Francesca Rinaldo ◽  
Jeffrey Jopling ◽  
Bingbin Liu ◽  
Rishab Mehra ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Computer Vision System

scholarly journals Segmentation for Complex Background Images using Deep Learning Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 1746-1750
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Vision System ◽  
Automated Segmentation ◽  
Computer Vision System ◽  
Important Stage ◽  
Deep Learning Algorithm ◽  
Complex Background ◽  
Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

A Robotic Prototype for Spraying and Pollinating Date Palm Trees

2008 ◽  
Author(s):  
Amir Shapiro ◽  
Eran Korkidi ◽  
Amit Rotenberg ◽  
Gilad Furst ◽  
Harel Namdar ◽  
...  
Keyword(s):  
Computer Vision ◽  
Date Palm ◽  
Vision System ◽  
Robotic Arm ◽  
Computer Vision System ◽  
Automatic Apparatus ◽  
Palm Trees ◽  
Risk Of Injury ◽  
Computer Controlled ◽  
Standard Tractor

Spraying and pollinating date palm trees is currently done manually by a team of three workers from a platform lifted 18 meters or more above the ground. This method is extremely unsafe and many accidents have happened due to lack of stability when the platform is in a lifted position. Alternatively, date clusters are occasionally sprayed by a large pressurized sprayer directly from the ground, a method that is highly unselective and environmentally harmful. In this paper we present the concept of an automatic apparatus that can effectively and accurately spray and pollinate date clusters from a robotic apparatus mounted to a standard tractor operated by a single driver. The apparatus consists of a robotic arm and computer controlled sprayer, guided by a computer vision system that detects and localizes date clusters with a camera. This system will minimize risk of injury, significantly save manpower (from three to one person per team), and deliver the spray with maximum accuracy thereby reducing chemical disposure. A small scaled prototype has been built and is currently under preliminary experiments.

Sequential additive manufacturing: automatic manipulation of 3D printed parts

2017 ◽  
Vol 23 (4) ◽  
pp. 653-659 ◽  
Author(s):  
Rafael Vidal Aroca ◽  
Carlos E.H. Ventura ◽  
Igor De Mello ◽  
Tatiana F.P.A.T. Pazelli
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Robotic Arm ◽  
Automation System ◽  
3D Printer ◽  
Computer Vision System ◽  
Content Type ◽  
3D Print ◽  
Speeded Up Robust Features ◽  
3D Printers

Purpose This paper aims to present a monitoring system and the usage of a robotic arm to remove finished parts of a three-dimensional (3D) printer build plate, enabling 3D printers to continuously build a sequence of parts. Design/methodology/approach The system relies on a 2-degree of freedom planar manipulator. The moment to remove printed parts from the printer build plate can be determined based on direct communication with the 3D printer control software or using information from a computer vision system that applies background subtraction and Speeded up Robust Features methods. Findings The proposed system automatically detects the end of standard 3D print jobs and controls the robotic arm to remove the part. Research limitations/implications Lighting variation can deteriorate the response of the computer vision system, which can be minimized using a controlled illumination environment. In addition, the printer build plate edges must be free so the parts can slip off the printer build plate when the robot pushes them out. Practical implications The system enables a more practical and automatized usage of 3D printers, reducing the need of human operators. Social implications The proposed system can reduce work hours of laboratory personnel, as there is no need to remove the printed parts manually before another job starts. Originality/value Computer vision system monitors the printing process and the automation system that enables continuous sequential 3D printing of parts. A prototype is described, which can be easily replicated with low cost parts.

Deep Learning in Agriculture as a Computer Vision System

2021 ◽  
pp. 225-236
Author(s):  
M. Senthamil Selvi ◽  
K. Deepa ◽  
N. Saranya ◽  
S. Jansi Rani
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Computer Vision System
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