A Real Time Object Detection in Integral Part of Computer Vision using Novel Image Classification of Faster R-CNN Algorithm over Fast R-CNN Algorithm

Abstract

Aim: The objective of the work is to increase the precision of object detection using novel image classification using machine learning algorithms. Materials and Methods: The categorising is performed by adopting a sample size of n = 10 in Faster RCNN (RPN) and sample size n = 10 in Fast R-CNN (Selective Search) algorithms with a sample size = 10 and the G-Power analysis was carried out with 80% and confidence interval 95%. Results and Discussion: The observation of the outcomes shows that the Faster R-CNN using region proposal networks has a high accuracy of 81.72% in comparison with the Selective Search based Fast R-CNN 79.61%. A statistically significant difference exists between the research groups with p=0.028 (2 tailed) (p<0.05). Conclusion: Detection of objects with high accuracy using machine learning algorithms shows that the regional proposal network based Faster R-CNN generates higher accuracy than the Selective search (Fast RCNN) algorithm.