A Survey of Modern Deep Learning based Object Detection Models

By Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Naveed Asghar, Brian Lee. Digit. Signal Process., 2021

918 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b8d83f56236294a3d1854e93f88a58ec218dfb87

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In Computer Vision and Pattern Recognition

Citations in the corpus

Large Selective Kernel Network for Remote Sensing Object Detection (2023, background): “Remote sensing object detection [47, 57, 82] focuses on identifying and locating objects of interest in aerial images, such as vehicles, ships or aircraft.”