Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study

This paper thoroughly explores the role of object detection in smart cities, specifically focusing on advancements in deep learning-based methods. Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges remain, such...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of engineering and applied science (Online) Vol. 71; no. 1; pp. 76 - 18
Main Authors:	Tian, Jiya, Jin, Qiangshan, Wang, Yizong, Yang, Jie, Zhang, Shuping, Sun, Dengxun
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024 Springer Nature B.V SpringerOpen
Subjects:	Accuracy Algorithms Autonomous vehicles Business metrics Cities Civil Engineering Classification COCO dataset Comparative studies Datasets Deep learning Electrical Engineering Engineering Engineering Mathematics Industrial Chemistry/Chemical Engineering Machine learning Mechanical Engineering Object detection Object recognition Performance analysis Performance evaluation Performance measurement Real time Robotics Robustness (mathematics) Smart cities Surveillance Telematics Thresholds Urban environments Deep learning Accuracy COCO dataset Performance analysis Object detection
ISSN:	1110-1903, 2536-9512
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!