A survey of modern deep learning based object detection models

• Published in: Digital signal processing Vol. 126; p. 103514
• Main Authors: Zaidi, Syed Sahil Abbas, Ansari, Mohammad Samar, Aslam, Asra, Kanwal, Nadia, Asghar, Mamoona, Lee, Brian
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 30.06.2022
• Subjects: Convolutional neural networks (CNN); Deep learning; Lightweight networks; Object detection and recognition; Object detection and recognition; Deep learning; Convolutional neural networks (CNN); Lightweight networks
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2022.103514

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.