SplitHDR: Saturation-Aware HDR Recovery and Denoising for Real-Time Detection

High Dynamic Range (HDR) imaging is indispensable forautonomous driving to ensure robust perception under changing light-ing conditions, such as tunnel exits or night driving. However, exist-ing learning-based HDR methods suffer from excessive computationalcosts and latency, making them impractical for real-time edge deploy-ment. In this paper, we propose SplitHDR, an extremely lightweightHDR framework tailored for machine vision. Leveraging advanced sen-sor technologies designed to minimize temporal displacement, our ap-proach avoids relying on heavy alignment modules and introduces aluminance-aware dynamic inference mechanism. Specifically, the modelconstructs a saturation mask based on input intensity, efficiently routingsaturated regions to a multi-frame fusion mode and unsaturated regionsto a lightweight denoising mode. This task decomposition ensures thatcomputational resources are allocated only where necessary. Experimen-tal results demonstrate that our method achieves state-of-the-art effi-ciency with only 0.002M parameters and 3.63 GMACs, reducing the pa-rameter count by over 99% compared to existing multi-exposure models.Despite the extreme compression, the HDR images reconstructed by ourapproach effectively preserve critical structural details, yielding highlycompetitive object detection performance crucial for safety-critical au-tonomous systems.