A Survey of Modern Deep Learning based Object Detection Models
It provides a comprehensive overview for researchers and practitioners in computer vision, but is incremental as it synthesizes existing work without introducing new methods.
This survey reviews recent deep learning-based object detection models, comparing their performance across multiple metrics and covering benchmark datasets, evaluation metrics, backbone architectures, and lightweight models for edge devices.
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.