• Dr.chandrashekhar S
• Dr.Basavaprasad B

Abstract: One of the important developments that sparked the deep neural network renaissance in computer vision, a subset of machine learning, was the development of CNNs. Typically, convolutional, pooling, and dense layers are combined to create a CNN. The moment we perceive an image, the human brain begins processing a massive amount of data. Every neuron has a distinct receptive field and is coupled to other neurons so that they collectively cover the whole visual field. Each neuron in a CNN processes data only in its receptive field, similar to how each neuron in the biological vision system responds to stimuli only in the constrained area of the visual field known as the receptive field. Simpler patterns like lines and curves are detected initially by the layers, followed by more intricate patterns like faces and objects. One can enable sight to computers by employing a CNN. The foundational component of the CNN is the convolution layer. It carries the majority of the computational load on the network. In order to increase the precision and energy efficiency of the detection process, this research examines algorithms created for real-time object detection applications by fusing Convolutional Neural Networks (CNN) with Scale Invariant Feature Transform. The scientific community has been interested in object detection for many years and has made great progress in this field. There is a vast array of applications that could benefit from more advancement in the field of object detection. The efforts in this area have been complimented by the field of machine learning's rapid development, and in recent years, the research community has made significant contributions to real-time object detection. Real-time object detection has been used in the current work, and the authors have worked to increase the detection mechanism's precision. Keywords: Kernel, Pattern, Machine learning, Object Detection, Deep Learning, CNN

"The organized Survey on Object Detection System Using CNN:A Theoretical Approach. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.g449-g453, July-2023, Available :http://www.jetir.org/papers/JETIR2307663.pdf

"The organized Survey on Object Detection System Using CNN:A Theoretical Approach. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppg449-g453, July-2023, Available at : http://www.jetir.org/papers/JETIR2307663.pdf