A 92m W 76.8GOPS vector matching processor with parallel Huffman decoder and query re-ordering buffer for real-time object recognition

2010 ◽  
Author(s):  
Seungjin Lee ◽  
Joonsoo Kwon ◽  
Jinwook Oh ◽  
Junyoung Park ◽  
Hoi-Jun Yoo
Keyword(s):  
Object Recognition ◽  
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.

scholarly journals Effective Parallelization Method for Object Recognition in 2D Sonar Images Based on Task Partitioning

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Ok-Kyoon Ha ◽  
Keonpyo Lee ◽  
Wan-Jin Kim ◽  
Kun Su Yoon
Keyword(s):  
Object Recognition ◽  
Parallel Processing ◽  
Real Time ◽  
Processing Method ◽  
Task Partitioning ◽  
Real Time Processing ◽  
Sequential Processing ◽  
Sonar Images ◽  
Graphical Processing ◽  
Imaging Sonar

Techniques for analyzing and avoiding hazardous objects and situations on the seabed are being developed to ensure the safety of ships and submersibles from various hazards. Improvements in accuracy and real-time response are critical for underwater object recognition, which rely on underwater sonar detection to remove noises and analyze the data. Therefore, parallel processing is being introduced for real-time processing of two-dimensional (2D) underwater sonar detector images for seabed monitoring. However, this requires optimized parallel processing between the modules for image processing and the data processing of a vast amount of data. This study proposes an effective parallel processing method, called Task Partitioning, based on central and graphical processing units for monitoring and identifying underwater objects in real time based on 2D-imaging sonar. The practicality of the proposed method is evaluated experimentally by comparing it to the sequential processing method. The experimental results show that the Task Partitioning method significantly improves the processing time for sonar images because it reduces the average execution time to 1% and 5% of the sequential processing method and general parallelization, respectively.

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