Real-Time Color Object Recognition and Navigation for QUARC QBOT2

2017 ◽  
Author(s):  
Waleed Obaid ◽  
Tamer Rabie ◽  
Mohammad Baziyad
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Color Object Recognition

A Real-Time and Effective Object Recognition and Localization Method

2014 ◽  
Vol 615 ◽  
pp. 107-112 ◽  
Author(s):  
Xiao Ling Ding ◽  
Qiang Zhao ◽  
Yi Bin Li ◽  
Xin Ma
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Color Image ◽  
Color Image Segmentation ◽  
Geometry Model ◽  
Perspective Geometry ◽  
Hsv Color Model ◽  
Camera Perspective ◽  
Color Object Recognition ◽  
Image Segmentation Algorithm

In this paper, we realize object recognition and localization in a real time based on appearance features of object. For object recognition, we proposed to use global feauture (color) of images, and with an improved color image segmentation algorithm to realize threshold segmentation based on pixels in the image’s HSV color model by using the tool OpenCV, so we can realize the special color object recognition. Further the object can be localized with the ground constrained method by using the camera perspective geometry model. In the lab conditions, we realized single color object recognition and localization by transplanting the algorithm into Amigobots mobile robot and proved this method is simple, effective and real-time.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

GPU processing for parallel image processing and real-time object recognition

2014 ◽  
Author(s):  
Kevin Vincent ◽  
Damien Nguyen ◽  
Brian Walker ◽  
Thomas Lu ◽  
Tien-Hsin Chao
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Real Time ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Gpu Processing

Object Recognition Pipeline

2018 ◽  
pp. 18-33
Author(s):  
John Alejandro Castro Vargas ◽  
Alberto Garcia Garcia ◽  
Sergiu Oprea ◽  
Sergio Orts Escolano ◽  
Jose Garcia Rodriguez
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Social Robots ◽  
Social Robot ◽  
Degree Of Disability ◽  
Domestic Environments ◽  
Domestic Assistance

Object grasping in domestic environments using social robots has an enormous potential to help dependent people with a certain degree of disability. In this chapter, the authors make use of the well-known Pepper social robot to carry out such task. They provide an integrated solution using ROS to recognize and grasp simple objects. That system was deployed on an accelerator platform (Jetson TX1) to be able to perform object recognition in real time using RGB-D sensors attached to the robot. By using the system, the authors prove that the Pepper robot shows a great potential for such domestic assistance tasks.

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