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Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., et al. (2021) Sparse R-CNN: End-to-End Object Detection with Learnable Proposals. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, 20-25 June 2021, 14449-14458.
https://doi.org/10.1109/CVPR46437.2021.01422

has been cited by the following article:

• TITLE: Overview of Object Detection Algorithms Using Convolutional Neural Networks

  AUTHORS: Junsong Ren, Yi Wang

  KEYWORDS: Deep Learning, Convolutional Neural Network, Object Detection, Computer Vision

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.1, January 29, 2022

  ABSTRACT: In today’s world, computer vision technology has become a very important direction in the field of Internet applications. As one of the basic problems of computer vision, object detection has become the basis of many vision tasks. Whether we need to realize the interaction between images and text or recognize fine categories, it provides reliable information. This article reviews the development of object detection networks. Starting from RCNN, we introduce object detection based on candidate regions, including Fast R-CNN, Faster R-CNN, etc.; and then start to introduce single-shot networks including YOLO, SSD, and Retina Net, etc. Detectors are the most excellent methods at present. By reviewing the current research status of object detection networks, it provides suggestions for the further development trend and research of object detection.