International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Object Detection Using Deep Learning Algorithm CNN

  • S. Sumahasan (Department of Computer Science and Engineering G.V.P.C.E.W) ;
  • Udaya Kumar Addanki (Department of Computer Science and Engineering G.V.P.C.E.W) ;
  • Navya Irlapati (Department of Computer Science and Engineering G.V.P.C.E.W) ;
  • Amulya Jonnala (Department of Computer Science and Engineering G.V.P.C.E.W)
  • Received : 2024.05.05
  • Published : 2024.05.30
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Abstract

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

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References

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