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Presentation object detection (1)
- 1. Smart factory: Object recognitionin industry By Sigma LABS
- 2. Introduction: Object recognition •Object recognition is an area of Artificial intelligence(AI) connected with identifying objects from video or image • It uses various algorithms/models to detect objects
- 3. Purpose • To builda flexible object and corresponding crack(damage) detecting system with high integrability • The common models and algorithms are (YOLO,GOTURN, Multibox etc.)
- 4. Problem • Most ofthe production industries are based on automated process that run with the help of scripts • Such type of systems have low flexibility, thus making them unable to locate, move or produce more accurately • Moreover, in critical situations such type of production units are unable of make necessary decisions without an operator
- 5. Solution • In orderto build a flexible production unit, we should give them ability to identify objects, corresponding damages and imperfections • Building a flexible system will allow the unit to control the production effectively by controlling the quality and sorting • Such type of system, will interact with production unit(For example: a belt conveyor)
- 6. Process • The cameraswill inspect details for imperfections (deformations, damages) and make appropriate decision with the help of a pretrained model
- 7. Hardware/software • Our imagedetection model will use Tensorflow, OpenCV library with the help of Python • Moreover, threading and multiprocessing is necessary • The object will be inspected on several cameras at high resolution (in order not to miss imperfections) • In order to effectively implement object recognition, we will use faster R-CNN developed my Microsoft
- 8. Architecture Faster R-CNN hasa Region Proposal Network (RPN) to generate a fixed set of regions. The RPN uses the convolutional features from the the image classification network, enabling nearly cost-free region proposals. The RPN is implemented as a fully convolutional network that predicts object bounds and objectness scores at each position. Further, we will be implementing functional API for multitask architecture in smart factory
- 9. Interaction • A PC(with installed pretrained model with the help of OpecCV, Tensorflow) which is connected with several cameras will seek for detail imperfections • More over it also interacts with the PLC(Programmable logic controller),a combination of hardware and software which controls the movement of conveyor • If an imperfect shaft has found, the signal will go to our PC through PCL and conveyor will stop and sort an imperfect detail
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- 12. Team • Scientific manager:Merey M. Sarsengeldin. Phd, associate professor, at Satbayev University and research scholar in Quantum machine Learning at University of Central Florida, founder of Sigma Labs LLP and Sigma LABS Florida, Orlando, US • Scientific manager: Abdullah S.Erdogan, PhD, Director of Sigma Labs Florida, Orlando, US. • Key manager : Zholdybaev Akezhan. Satbayev University, Sigma Labs. • Key manager: Haluk Laman, Ph.d. Project engineer at HNTB, Ocoee, Florida. A Civil Transportation Engineer with 7 plus years of experience in ITS, Traffic Simulation Modeling, Traffic Operations and Safety, Planning, Data Science and Statistics. Member of Sigma labs • Developers and team for fulfilling the project http://sigmalabs.info/about.html
- 13. Financial support request •Equipment:53 040 $ • Salary(annual):39 780 $ • Overhead expenses: 9 282 $
- 14. About Sigma LABS SigmaLABS is a scientific organization focused on science and disruptive technology (data science, blockchain, IOT and their applications). For more info please see http://sigmalabs.info
- 15. Collaboration with Universityof Central Florida