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  Published Paper Details:

  Paper Title

OBJECT DETECTION USING CONVOLUTIONAL NEURAL NETWORK

  Authors

  Kolluri Bhavesh,  Patade Omkar,  Rajapkar Adesh,  Saroja T. V.

  Keywords

computer vision, convolutional neural networks, image classification, object discovery, transfer learning

  Abstract


Viewing systems are essential for building a mobile robot that will complete a task such as navigation, surveillance, and explosive disposal (EOD). This will let the robots controller or operator know what's in nature and perform the following tasks. With the recent development of deep neural networks in image processing, distortion and precision detection are now possible. In this paper, Convolutional Neural Networks (CNN) is used to find natural objects. Two types of art models are compared for object acquisition, Single Shot Multi-Box Detector (SSD) with MobileNetV1 and Faster Region-based Convolutional Neural Network (Faster-RCNN) with InceptionV2. The result shows that one model is suitable for real-time use because of the speed and the other can be used to get something more accurate.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2104075

  Paper ID - 205326

  Page Number(s) - 509-514

  Pubished in - Volume 9 | Issue 4 | April 2021

  DOI (Digital Object Identifier) -   

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

  E-ISSN Number - 2320-2882

  Cite this article

  Kolluri Bhavesh,  Patade Omkar,  Rajapkar Adesh,  Saroja T. V.,   "OBJECT DETECTION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 4, pp.509-514, April 2021, Available at :http://www.ijcrt.org/papers/IJCRT2104075.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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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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