Problem
Research questions and friction points this paper is trying to address.
High-Dynamic-Range
Object Detection
Tone Mapping
Embedded Systems
Autonomous Driving
Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection
🤖 AI Summary
本文提出了一种新的色调映射方法，通过神经光度校准和局部色调映射模型解决高动态范围图像在物体检测中的性能下降问题。
📝 Abstract
High-dynamic-range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particularly in autonomous driving. However, most neural networks for embedded systems are trained on low-dynamic-range (LDR) inputs and suffer substantial performance degradation when handling high-bit-depth HDR images due to the challenges posed by extreme dynamic ranges. In this paper, we propose a novel tone mapping method that not only bridges the gap between HDR RAW inputs and the LDR sRGB requirements of detection networks but also achieves end-to-end optimization with downstream tasks. Instead of relying on the traditional image signal processing (ISP) pipeline, we introduce neural photometric calibration to regularize dynamic ranges and a scaling-invariant local tone mapping model to preserve image details. In addition, our architecture also supports performance transfer finetuning, enabling efficient adaptation from the LDR sRGB images to the HDR RAW images with minimal cost. The proposed method outperforms traditional tone mapping algorithms and advanced AI-ISP methods in challenging automotive HDR scenes. Moreover, our pipeline achieves real-time processing of 4K high-bit-depth HDR inputs on NVIDIA Jetson platforms.
Innovation
Methods, ideas, or system contributions that make the work stand out.
neural photometric calibration
scaling-invariant local tone mapping model
end-to-end optimization
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