scholarly journals Implementation project of object recognition system to the modular didactic system of manufacturing gearings

2021 ◽  
Vol 5 (1) ◽  
pp. 39-44
Author(s):  
Tomasz Chaciński ◽  
Bartłomiej Wietrak
Keyword(s):  
Object Recognition ◽  
Production Process ◽  
Production System ◽  
3D Model ◽  
Vision System ◽  
Recognition System ◽  
Modular Structure ◽  
System Operation ◽  
Implementation Project ◽  
System Components

The main goal of the work was to create a project to implement an object recognition system into a modular didactic gear production system. The project shows how, thanks to the modular structure of the gear system, it is possible to easily add new elements to it, at the same time increasing its capabilities. At the beginning, the characteristics of the system were presented before the implementation, including a description of the production process that takes place in this system and all modules of the system were exchanged. Then the vision system for object recognition and all its components were described. The technical-organizational project of the implementation presented the concept of the deployment of the system modules and the principle of system operation after the implementation. A 3D model of all system components was also presented.

A 3-D Object Recognition System Based on a Rapid 3-D Vision System 1

2003 ◽  
Vol 36 (24) ◽  
pp. 269-274
Author(s):  
Seungjoon Choi ◽  
Hongpyo Park ◽  
Sungjin Kim ◽  
Sungchan Park ◽  
Sangchul Won ◽  
...  
Keyword(s):  
Object Recognition ◽  
Vision System ◽  
Recognition System ◽  
System 1

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.

ViObject: A Smartwatch-based Object Recognition System via Vibrations

2021 ◽  
Author(s):  
Wenqiang Chen ◽  
Daniel Bevan ◽  
John Stankovic
Keyword(s):  
Object Recognition ◽  
Recognition System

scholarly journals ANALISA PERBANDINGAN PENJADWALAN PRODUKSI DENGAN METODE CDS (CAMPBELL DUDEK SMITH), EDD (EARLIEST DUE DATE) DAN FCFS (FIRST COME FIRST SERVE) PADA PT. SARI WARNA ASLI UNIT V

2021 ◽  
Vol 1 (2) ◽  
pp. 46-51
Author(s):  
Dwi Ayu Lestari, Vikha Indira Asri
Keyword(s):  
Production Process ◽  
Production System ◽  
Production Scheduling ◽  
Job Shop ◽  
Allocation Of Resources ◽  
Due Date ◽  
Scheduling System ◽  
Make To Order ◽  
First Come First Serve ◽  
Scheduling Method

Scheduling is defined as the process of sequencing the manufacture of a product as a whole on several machines. All industries need proper scheduling to manage the allocation of resources so that the production system can run quickly and precisely as of it can produce optimal product. PT. Sari Warna Asli Unit V is one of the companies that implements a make to order production system with the FCFS system. Thus, scheduling the production process at this company is also known as job shop production scheduling. The methods used in this research are the CDS method, the EDD method and the FCFS method. The purpose of this research is to minimize the production time and determine the best method that can be applied to the company. The results of this research showed that the makespan obtained in the company's scheduling system with FCFS rules was 458 minutes, and the results of scheduling using the CDS method obtained a makespan value of 329 minutes, then the best production scheduling method that had the smallest makespan value was the CDS method.

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