TMO-Det: Deep tone-mapping optimized with and for object detection

被引:2
作者
Kocdemir, Ismail Hakki [1 ,2 ]
Koz, Alper [3 ]
Akyuz, Ahmet Oguz [1 ,2 ]
Chalmers, Alan [4 ]
Alatan, Aydin [2 ,5 ]
Kalkan, Sinan [1 ,2 ]
机构
[1] METU, Dept Comp Engn, Ankara, Turkiye
[2] METU, Ctr Image Anal OGAM, Ankara, Turkiye
[3] METU, Ctr Image Anal OGAM, Ankara, Turkiye
[4] Univ Warwick, WMG, Coventry, England
[5] METU, Dept Elect Elect Engn, Ankara, Turkiye
关键词
Object detection; High dynamic range; Low dynamic range; Tone-Mapping; Generative adversarial networks; NETWORK;
D O I
10.1016/j.patrec.2023.06.017
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.& COPY; 2023 Elsevier B.V. All rights reserved.
引用
收藏
页码:230 / 236
页数:7
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