EP-SAM: An Edge-Detection Prompt SAM Based Efficient Framework for Ultra-Low Light Video Segmentation
Zhitao Wang, Jiangtao Wen, Yuxing Han
Abstract
The Segment Anything Model (SAM) excels at generating high-quality object masks with various prompts but struggles in ultra-low light. We developed EP-SAM (Edge-Detection Prompt SAM) with a Low Light Edge-Detection Network (LLEN), offering strong robustness and lightweight performance in ultra-low light. Using edge information as prompts, EP-SAM achieves high-precision segmentation in low-light videos.By combining motion estimation with reference frame optimization, the initial frame can predict the next 29 frames, reducing inference time by over 80% and computational complexity by 86%. Tests show LLEN accurately extracts edges even with about 80 photons per pixel, enabling EP-SAM to produce precise masks and significantly outperform SAM and SAM-2. EP-SAM improves mean Intersection over Union (mIoU) by 5.85% over SAM on the CamVid dataset. Video demos: https://github.com/wzt22thu/EP-SAM/releases/tag/DEMO.
BibTeX
@inproceedings{icassp2025_epsamanedgedetec,
title = {EP-SAM: An Edge-Detection Prompt SAM Based Efficient Framework for Ultra-Low Light Video Segmentation},
author = {Zhitao Wang and Jiangtao Wen and Yuxing Han},
booktitle = {ICASSP 2025},
year = {2025}
}