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

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.