# A survey of modern deep learning based object detection models #paper
1. paper-info
1.1 Metadata
- Author:: [[Syed Sahil Abbas Zaidi]], [[Mohammad Samar Ansari]], [[Asra Aslam]], [[Nadia Kanwal]], [[Mamoona Asghar]], [[Brian Lee]]
- 作者机构::
- Keywords:: #CNN , #ObjectDetection
- Journal:: [[Digital Signal Processing]]
- Date:: [[2022-06-30]]
- 状态:: #Done
- 链接:: https://www.sciencedirect.com/science/article/pii/S1051200422001312
- 修改时间:: 2022.12.26
1.2. 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.
2. 总结
Fig. 1思维导图
标签：based,2022,models,object,deep,detection,learning From： https://www.cnblogs.com/guixu/p/17006255.html