Optimized Machine Learning Pipeline for Object Detection in Automotive and Surveillance Systems

Nidhi Joshi Parsai, Sumit Jain, Ayesha Sharma, Swapnil Waghela

2025

Abstract

Object detection is the fundamental building block of computer vision, and a key enabler of automotive safety systems and video surveillance. It comes to fast object detection pipeline and proposes an efficient detection using YOLO for near real-time performance and Faster R-CNN localization for accuracy. It addresses optimal speed versus accuracy trade-off coverage in adverse lighting and strong occlusion conditions. The proposed approach shows that by using sophisticated pre-processing techniques as well as CNNs for feature extraction, the proposed system can maintain steady performance in a set of scenarios. Together, this aspect renders this hybrid method flexible enough to be used for various operational needs and thus can sensibly be deployed on real-time and large-scale settings. The study shows how machine learning can offer speed, accuracy, and reliability for challenging applications, like the classification of object.

Paper Citation

in Harvard Style

Parsai N., Jain S., Sharma A. and Waghela S. (2025). Optimized Machine Learning Pipeline for Object Detection in Automotive and Surveillance Systems. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 27-37. DOI: 10.5220/0013876300004919

in Bibtex Style

@conference{icrdicct`2525,
author={Nidhi Parsai and Sumit Jain and Ayesha Sharma and Swapnil Waghela},
title={Optimized Machine Learning Pipeline for Object Detection in Automotive and Surveillance Systems},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={27-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013876300004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Optimized Machine Learning Pipeline for Object Detection in Automotive and Surveillance Systems
SN - 978-989-758-777-1
AU - Parsai N.
AU - Jain S.
AU - Sharma A.
AU - Waghela S.
PY - 2025
SP - 27
EP - 37
DO - 10.5220/0013876300004919
PB - SciTePress