Real-Time Object Recognition Using Deep-Learning

Authors

  • Herman Khalid Omar Department of Computer Science, Nawroz University, Duhok, Kurdistan Region - IRAQ
  • Shahad Fauzi Mohammed Department of Computer Science, Nawroz University, Duhok, Kurdistan Region - IRAQ
  • Rana Adib Khisro Department of Computer Science, Nawroz University, Duhok, Kurdistan Region - IRAQ

DOI:

https://doi.org/10.25007/ajnu.v10n2a1073

Keywords:

Keywords: Deep Learning, Convolutional Neural Network (CNN), Feature Extraction, Classification

Abstract

The great interest at the moment is focused on the field of technology, especially artificial intelligence, also do not devoid of our daily life of the use of phone applications and computer programs, that increasing of phone and computer usage demands more programs and applications to satisfy the needs of users. However, this approach starts with acquirement an image of a particular medical or engineering tool, is displayed on the computer through a webcam, whether this image is a photo or digital through a display screen, or even the device itself, the computer will identify the tool and a simplified explanation of the way it works with a video demonstration throughout MATLAB IDE for implementing this project as well as easy to use by anyone even the user doesn’t have any experience software.     

Finally, this approach has been created this project to save time and effort for the users instead of searching on a specific tool that they need about its name, how to use so we tried to facility this matter. The proposed algorithm got accurate result, for the doctor’s tools the accuracy was approximately between 95.8 %, for the engineer’s tools was approximately between 98.3 % and for mix of them was approximately between 94.1 %.

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Published

2021-05-08

How to Cite

Omar, H. K., Mohammed, S. F., & Khisro, R. A. (2021). Real-Time Object Recognition Using Deep-Learning. Academic Journal of Nawroz University, 10(2), 47–53. https://doi.org/10.25007/ajnu.v10n2a1073

Issue

Vol. 10 No. 2 (2021)

Section

Articles

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