A Survey of Modern Deep Learning based Object Detection Models

Abstract

Object Detection is the task of classification and
localization of objects in an image or video. It has gained
prominence in recent years due to its widespread applications.
This article surveys recent developments in deep learning based
object detectors. Concise overview of benchmark datasets and
evaluation metrics used in detection is also provided along with
some of the prominent backbone architectures used in recognition
tasks. It also covers contemporary lightweight classification models used on edge devices. Lastly, we compare the performances
of these architectures on multiple metrics

Citation

Zaidi, S., Ansari, M., Aslam, A., Kanwal, N., Asghar, M., & Lee, B. (2022). A Survey of Modern Deep Learning based Object Detection Models. Digital Signal Processing, 1-19. https://doi.org/10.1016/j.dsp.2022.103514

• Acceptance Date: Apr 24, 2021
• Online Publication Date: Mar 8, 2022
• Publication Date: Jun 30, 2022
• Publicly Available Date: Mar 9, 2024
• Journal: Digital Signal Processing
• Print ISSN: 1051-2004
• Electronic ISSN: 1095-4333
• Publisher: Elsevier
• Pages: 1-19
• DOI: https://doi.org/10.1016/j.dsp.2022.103514
• Public URL: https://keele-repository.worktribe.com/output/421409
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1051200422001312?via%3Dihub