Enhanced Object Detection with Deep CNN for Advance Driving Assistance
Author(s):
Syed Talha Amaan, S Udaya Hindu, Rahulnaik Barmavath, Dr. Sreedhar Bhukya
Keywords:
Self-driving, Autonomous, Explainable-AI, Convolutional neural networks
Abstract
The intelligent vehicle, as a crucial technology in the intelligent transportation system, is the bearer of a complete integration of several technologies. Although vision-based autonomous driving has showed great promise, there is still the issue of analysing the complex traffic scenario using the data acquired. Recently, self driving has been broken down into many tasks utilising various models, such as object detection and intention identification. In this work, a vision-based system was built to recognise and identify numerous items in the traffic scene, as well as forecast pedestrian intentions. The key contributions of this study are: (1) A fine-tuned Part-Affinity-Field's approach to know pedestrian pose was proposed; (3) Explainable-AI (XAI) technology was used to explain and assist the estimation results in the risk assessment phase; and (4) a large autonomous driving dataset with several subsets for each task was brought in; and (5) an endtoend system having multiple models with high accuracy was developed. The overall parameters of the modified Faster RCNN were decreased by 74%, demonstrating that it meets the real-time capabilities. Furthermore, when compared to the state-of-the-art, the detection precision of the enhanced Faster RCNN improved by 2.6 percent.
Article Details
Unique Paper ID: 155467

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 975 - 978
Article Preview & Download


Share This Article

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Latest Publication

Go To Issue



Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies