让车载摄像头的高动态图像实时适配检测模型，提升自动驾驶视觉性能。
Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection
- 用神经光照校准与不变局部映射，直接优化下游检测任务。
- 在4K HDR图像上实现每秒30帧以上实时处理，推理延迟低于33毫秒。
- 支持轻量微调，从低动态图像快速适配高动态输入，适合嵌入式部署。
高动态范围（HDR）图像具有丰富的色调和细节表现力，在自动驾驶等场景中具有巨大潜力。然而，大多数面向嵌入式系统的神经网络均基于低动态范围（LDR）输入训练，面对高比特深度的HDR图像时性能显著下降，主要源于极端动态范围带来的挑战。本文提出一种新型色调映射方法，不仅将HDR RAW输入与检测网络所需的LDR sRGB格式对齐，还实现了与下游任务的端到端优化。不同于传统图像信号处理（ISP）流程，我们引入神经光照校准以规范动态范围，并采用尺度不变的局部色调映射模型保留图像细节。此外，该架构支持性能迁移微调，可仅用极少计算成本，将模型从LDR sRGB图像高效适配至HDR RAW输入。所提方法在复杂汽车级HDR场景中优于传统色调映射算法及先进AI-ISP方法。同时，该流水线可在NVIDIA Jetson平台上实现实时处理4K高比特深度HDR输入，达到30帧/秒以上，延迟低于33毫秒。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。