A comprehensive survey of the R-CNN family for object detection
Authors:
- 12 December 2022
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Abstract
Object detection using deep learning, one of the most challenging problems in computer vision, seeks to locate instances of objects from a large number of predefined categories in natural images. Given this period of rapid evolution, the main contribution of this paper is to provide a comprehensive survey of the region-based convolutional neural network (R-CNN) family (R-CNN, Fast R-CNN, and Faster R-CNN). In comparison to the R-CNN and Fast R-CNN, simulation results show that the faster R-CNN improves not only accuracy but also detection speed. For robust object detection, it has been found that the Faster R-CNN is particularly suited for this purpose. We conclude with several open issues and challenges that are keys to the design of future work.Keywords
This publication has 13 references indexed in Scilit:
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Deep Residual Learning for Image Recognition2016
- A closer look at Faster R-CNN for vehicle detection2016
- Deep learning for visual understanding: A reviewNeurocomputing, 2016
- Fast R-CNN2015
- Fully convolutional networks for semantic segmentation2015
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation2014
- Selective Search for Object RecognitionInternational Journal of Computer Vision, 2013
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998
- Neocognitron: A hierarchical neural network capable of visual pattern recognitionNeural Networks, 1988