Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/1203
Title: Semantic Segmentation Based Object Detection for Autonomous Driving
Authors: Maiti, Tapas Kumar
Prajapati, Harsh
Keywords: Autonomous driving
Semantic segment
Computer vision
PyTorch framework
Robot operating system
Raspberry Pi
Issue Date: 2023
Publisher: Dhirubhai Ambani Institute of Information and Communication Technology
Citation: Prajapati, Harsh (2023). Semantic Segmentation Based Object Detection for Autonomous Driving. Dhirubhai Ambani Institute of Information and Communication Technology. ix, 63 p. (Acc. # T01144).
Abstract: This research focuses on solving the autonomous driving problem which is necessaryto fulfill the increasing demand of autonomous systems in today�s world.The key aspect in addressing this challenge is the real-time identification andrecognition of objects within the driving environment. To accomplish this, weemploy the semantic segmentation technique, integrating computer vision, machinelearning, deep learning, the PyTorch framework, image processing, and therobot operating system (ROS). Our approach involves creating an experimentalsetup using an edge device, specifically a Raspberry Pi, in conjunction with theROS framework. By deploying a deep learning model on the edge device, we aimto build a robust and efficient autonomous system that can accurately identifyand recognize objects in real time.
URI: http://drsr.daiict.ac.in//handle/123456789/1203
Appears in Collections:M Tech Dissertations

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