Abstract
AbstractIn recent years, instance segmentation has become a key research area in computer vision. This technology has been applied in varied applications such as robotics, healthcare and intelligent driving. Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Further, we will discuss about its development in this field along with the most common datasets used. We will also focus on different challenges and future development scope for instance segmentation. This technology will provide a strong reference for future researchers in our survey paper.
Funder
University of Technology Sydney
Publisher
Springer Science and Business Media LLC
Reference136 articles.
1. Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097–105.
2. Zeiler M, Fergus R. Visualizing and understanding convolutional networks. In: European conference on computer vision, pp. 818–833. 2014
3. LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.
4. Albawi S, Mohammed T, Al-Zawi S. Understanding of a convolutional neural network. In: 2017 International conference on engineering and technology (ICET), pp. 1–6. 2017.
5. O’Shea K, Nash R. An introduction to convolutional neural networks (2015). arXiv preprint arXiv:1511.08458.
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