• Gouri Asma Khanam
• Tudumu Reddi Rani
• Syed Shamar Sulthana
• Wilson James HimaBindu

A significant yet unfinished job is the creation of a surface recognition system for automobiles. The purpose of this research is to show how useful millimeter wave radar can be for surface discrimination in automotive sensing by analyzing images of actual road surfaces captured with a 79 GHz imaging radar and applying a novel approach to surface classification. The proposed experimental method utilizes a convolutional neural network to accurately categorize surfaces. Convolutional neural networks (CNNs) are a kind of Deep Learning method that can take an input image, give significance (learnable weights and biases), and then detect various characteristics and objects within that image. The convolutional neural network that autonomously learns unique characteristics for each class.

Millimeter wave radar, radar remote sensing, radar imaging, electromagnetic scattering, convolutional neural network.

" Radar Imaging Based Road Surface Classification Using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.c892-c896, July-2023, Available :http://www.jetir.org/papers/JETIR2307298.pdf

" Radar Imaging Based Road Surface Classification Using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppc892-c896, July-2023, Available at : http://www.jetir.org/papers/JETIR2307298.pdf