Active Reinforcement Learning for the Semantic Segmentation of Urban Images
Image segmentation using supervised learning algorithms usually requires large amounts of annotated training data, while urban datasets frequently contain unbalanced classes leading to poor detection of under-represented classes. We investigate the use of a reinforced active learning method to addre...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2024.2374788 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850034540229165056 |
|---|---|
| author | Mahya Jodeiri Rad Costas Armenakis |
| author_facet | Mahya Jodeiri Rad Costas Armenakis |
| author_sort | Mahya Jodeiri Rad |
| collection | DOAJ |
| description | Image segmentation using supervised learning algorithms usually requires large amounts of annotated training data, while urban datasets frequently contain unbalanced classes leading to poor detection of under-represented classes. We investigate the use of a reinforced active learning method to address the limitations of semantic segmentation on complex urban scenes. In this method, an agent learns to select small informative regions of the image to be labeled from a pool of unlabeled data. The agent is represented by a deep Q-Network, where a Markov Decision Process (MDP) is used to formulate the Active Learning problem. We introduced the Frequency Weighted Average IoU (FWA IoU) as the image region selection performance metric to reduce the amount of training data while achieving competitive results. Using the Cityscapes and GTAv urban datasets, three baseline image segmentation networks (FPN, DeepLabV3, DeepLabV3+) trained with image regions selected by the proposed FWA IoU metric performed better compared to baseline region selection by active learning methods such as the Random selection, Entropy-based selection, and Bayesian Active Learning by Disagreement. Training performance equivalent to 98% of the fully supervised segmentation network was achieved by labeling only 8% of the dataset. |
| format | Article |
| id | doaj-art-38db7e0847be475d97aab3755b94b479 |
| institution | DOAJ |
| issn | 1712-7971 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj-art-38db7e0847be475d97aab3755b94b4792025-08-20T02:57:47ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712024-12-0150110.1080/07038992.2024.23747882374788Active Reinforcement Learning for the Semantic Segmentation of Urban ImagesMahya Jodeiri Rad0Costas Armenakis1Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York UniversityDepartment of Earth and Space Science and Engineering, Lassonde School of Engineering, York UniversityImage segmentation using supervised learning algorithms usually requires large amounts of annotated training data, while urban datasets frequently contain unbalanced classes leading to poor detection of under-represented classes. We investigate the use of a reinforced active learning method to address the limitations of semantic segmentation on complex urban scenes. In this method, an agent learns to select small informative regions of the image to be labeled from a pool of unlabeled data. The agent is represented by a deep Q-Network, where a Markov Decision Process (MDP) is used to formulate the Active Learning problem. We introduced the Frequency Weighted Average IoU (FWA IoU) as the image region selection performance metric to reduce the amount of training data while achieving competitive results. Using the Cityscapes and GTAv urban datasets, three baseline image segmentation networks (FPN, DeepLabV3, DeepLabV3+) trained with image regions selected by the proposed FWA IoU metric performed better compared to baseline region selection by active learning methods such as the Random selection, Entropy-based selection, and Bayesian Active Learning by Disagreement. Training performance equivalent to 98% of the fully supervised segmentation network was achieved by labeling only 8% of the dataset.http://dx.doi.org/10.1080/07038992.2024.2374788 |
| spellingShingle | Mahya Jodeiri Rad Costas Armenakis Active Reinforcement Learning for the Semantic Segmentation of Urban Images Canadian Journal of Remote Sensing |
| title | Active Reinforcement Learning for the Semantic Segmentation of Urban Images |
| title_full | Active Reinforcement Learning for the Semantic Segmentation of Urban Images |
| title_fullStr | Active Reinforcement Learning for the Semantic Segmentation of Urban Images |
| title_full_unstemmed | Active Reinforcement Learning for the Semantic Segmentation of Urban Images |
| title_short | Active Reinforcement Learning for the Semantic Segmentation of Urban Images |
| title_sort | active reinforcement learning for the semantic segmentation of urban images |
| url | http://dx.doi.org/10.1080/07038992.2024.2374788 |
| work_keys_str_mv | AT mahyajodeirirad activereinforcementlearningforthesemanticsegmentationofurbanimages AT costasarmenakis activereinforcementlearningforthesemanticsegmentationofurbanimages |