Comparative Analysis of Two-Stage and One-Stage Object Detection Models

Yibo Han

2024

Abstract

The background and basic ideas of object detection are examined in this essay, with a focus on contrasting one-stage and two-stage detectors. The main goal is to compare and contrast the accuracy and speed of different object identification methods. The study covers a thorough examination of models including Single Shot Multi-Box Detector (SSD), You Only Look Once (YOLO), and the Region Convolution Neural Network (RCNN) series. The study involves preprocessing data from the Pattern Analysis, Statistical modelling and Computational Learning Visual Object Classes (PASCAL VOC), Image Network (ImageNet), Microsoft Common Objects in Context (MS COCO), and Open Images datasets, followed by the application and training of different detection algorithms. Performance metrics, including Mean Average Precision (MAP) and Frames Per Second (FPS), are used to assess the models. The findings indicate that two-stage detectors, such as Faster R-CNN, process information more slowly yet achieve better detection accuracy, especially in complex scenarios. On the other hand, one-stage detectors, such YOLO and SSD, have quicker inference times and are therefore better suited for real-time applications, but precision is usually lost, particularly when identifying smaller objects. This study holds significant implications for fields requiring high-performance object detection, like medical imaging and driverless driving.

Paper Citation

in Harvard Style

Han Y. (2024). Comparative Analysis of Two-Stage and One-Stage Object Detection Models. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 289-294. DOI: 10.5220/0013515900004619

in Bibtex Style

@conference{daml24,
author={Yibo Han},
title={Comparative Analysis of Two-Stage and One-Stage Object Detection Models},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={289-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013515900004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Comparative Analysis of Two-Stage and One-Stage Object Detection Models
SN - 978-989-758-754-2
AU - Han Y.
PY - 2024
SP - 289
EP - 294
DO - 10.5220/0013515900004619
PB - SciTePress