Object Detection for Autonomous Driving: High-Dynamic Range vs. Low-Dynamic Range Images

Date

2022-01-01

Author

Kocdemir, Ismail H.
Akyüz, Ahmet Oğuz
Koz, Alper
Chalmers, Alan
Alatan, Abdullah Aydın
Kalkan, Sinan

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An important problem in autonomous driving is to perceive objects even under challenging illumination conditions. Despite this problem, existing solutions use low-dynamic range (LDR) images for object detection for autonomous driving. In this paper, we provide a novel analysis on whether high-dynamic range (HDR) images can provide better performance for object detection for autonomous driving. To this end, we choose a seminal deep object detector and systematically evaluate its performance when trained with (i) LDR images, (ii) HDR images, and (iii) tone-mapped LDR images for scenes with different illuminations. We show that a detector with HDR images pre-processed with normalization and gamma correction can only marginally perform better than a detector with LDR or tone-mapped LDR images. Our analysis of this unexpected finding reveals that a detector with HDR images requires significantly more samples as the space of HDR images is significantly larger than that of LDR images.

Subject Keywords

Autonomous Driving, Object Detection, High-Dynamic Range (HDR), Low-Dynamic Range (LDR), Autonomous Driving, High-Dynamic Range (HDR), Low-Dynamic Range (LDR), Object Detection

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143629419&origin=inward
https://hdl.handle.net/11511/101413

DOI

https://doi.org/10.1109/mmsp55362.2022.9949582

Conference Name

24th IEEE International Workshop on Multimedia Signal Processing, MMSP 2022

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

I. H. Kocdemir, A. O. Akyüz, A. Koz, A. Chalmers, A. A. Alatan, and S. Kalkan, “Object Detection for Autonomous Driving: High-Dynamic Range vs. Low-Dynamic Range Images,” presented at the 24th IEEE International Workshop on Multimedia Signal Processing, MMSP 2022, Shanghai, Çin, 2022, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143629419&origin=inward.