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 |
Files in This Item:
File | Size | Format | |
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202111074.pdf | 20.26 MB | Adobe PDF | View/Open |
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