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TMO-Det : deep tone-mapping optimized with and for object detection
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Kocdemir, Ismail Hakki, Koz, Alper, Akyuz, Ahmet Oguz, Chalmers, Alan, Alatan, Aydin and Kalkan, Sinan (2023) TMO-Det : deep tone-mapping optimized with and for object detection. Pattern Recognition Letters, 172 . pp. 230-236. doi:10.1016/j.patrec.2023.06.017 ISSN 0167-8655.
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Official URL: http://dx.doi.org/10.1016/j.patrec.2023.06.017
Abstract
Detecting objects in challenging illumination conditions is critical for autonomous driving. Existing solutions detect objects with standard or tone-mapped Low Dynamic Range (LDR) images. In this paper, we propose a novel adversarial approach that jointly optimizes tone-mapping (mapping High Dynamic Range (HDR) to LDR) and object detection. We analyze different ways to combine the feedback from tone-mapping quality and object detection quality for training such an adversarial network. We show that our deep tone-mapping operator jointly trained with an object detector achieves the best tone-mapping quality as well as detection quality compared to alternative approaches.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Journal or Publication Title: | Pattern Recognition Letters | ||||||||
Publisher: | Elsevier BV | ||||||||
ISSN: | 0167-8655 | ||||||||
Official Date: | August 2023 | ||||||||
Dates: |
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Volume: | 172 | ||||||||
Page Range: | pp. 230-236 | ||||||||
DOI: | 10.1016/j.patrec.2023.06.017 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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