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  Published Paper Details:

  Paper Title

COMPONENT DETECTION WITH DEEP LEARNING A REVIEW

  Authors

  Harshal Jorigal,  Shivani Thorat,  Karuna Rathod,  Nikita Doiphode,  Mrs. Geeta Atkar

  Keywords

computer vision, OpenCv, Tensorflow, Keras, yolo, CNN.

  Abstract


Object detection is a computerized technology-related Computer vision and image processing. It is a technique that allows us to identify and locate objects in an image or video. With the help of this technology the identification and localization of objects or any component can be simplified, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. The goal of object detection is to replicate this intelligence using a computer. Object detection can be done more accurately using certain algorithms. OpenCV which is a python library that functions mainly on computer vision, Yolo is the most efficient real-time algorithm which is used to effectively detect an object. In deep learning, CNN TensorFlow Keras is the algorithms used to accurately locate and identify objects.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTI020039

  Paper ID - 211576

  Page Number(s) - 188-196

  Pubished in - Volume 9 | Issue 11 | November 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Harshal Jorigal,  Shivani Thorat,  Karuna Rathod,  Nikita Doiphode,  Mrs. Geeta Atkar,   "COMPONENT DETECTION WITH DEEP LEARNING A REVIEW", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 11, pp.188-196, November 2021, Available at :http://www.ijcrt.org/papers/IJCRTI020039.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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