TMO-Det: Deep tone-mapping optimized with and for object detection
Pattern Recognition Letters, cilt.172, ss.230-236, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 172
- Basım Tarihi: 2023
- Doi Numarası: 10.1016/j.patrec.2023.06.017
- Dergi Adı: Pattern Recognition Letters
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
- Sayfa Sayıları: ss.230-236
- Anahtar Kelimeler: Generative adversarial networks, High dynamic range, Low dynamic range, Object detection, Tone-Mapping
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
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.