An Enhanced Object Detection in Integral Part of Computer Vision using object Localization by Comparing Spatial Pyramid Pooling Net Algorithm over R-CNN Algorithm.

Authors

  • M.Srikar
  • K. Malathi

DOI:

https://doi.org/10.47750/pnr.2022.13.S04.203

Keywords:

Object Detection, R-CNN, Deep Convolutional Neural Network, Novel Pyramid Pooling Layer, Object Localization, Deep Network, Spatial Pyramid Match.

Abstract

Aim: The objective of the work is to increase the precision of detecting objects using inventive Object localisation and Deep Convolutional Neural Networks with machine learning algorithms. Materials and Methods: The categorising is performed by adopting a sample size of n = 10 in Spatial Pyramid Pooling net in R-CNN and sample size n = 10 in R-CNN algorithms with a sample size = 10 and the G-Power analysis was carried out with 80% and the confidence interval 95%. Results and Discussion: The observation of the outcomes shows that the R-CNN using spatial pyramid pooling net layer has a high accuracy of 85.43% in comparison with the Region based convolutional neural network 78.36%. A statistically significant difference exists between the research groups with p=0.001 (2 tailed) (p<0.05). Conclusion: Detection of objects with high accuracy using machine learning shows that the spatial pyramid pooling net layer based R-CNN generates higher accuracy than region based convolutional neural network algorithms.

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Published

2022-10-07

Issue

Section

Articles

How to Cite

An Enhanced Object Detection in Integral Part of Computer Vision using object Localization by Comparing Spatial Pyramid Pooling Net Algorithm over R-CNN Algorithm. (2022). Journal of Pharmaceutical Negative Results, 1694-1700. https://doi.org/10.47750/pnr.2022.13.S04.203