用回归方法快速找到适合边缘设备的轻量遥感分割模型
SatReg: Regression-based Neural Architecture Search for Lightweight Satellite Image Segmentation
- 基于回归的搜索框架，只调两个宽度参数
- 在Jetson Orin Nano上实现低延迟高能效部署
- 适合想在卫星边缘端高效部署分割模型的研究者
随着地球观测任务向机载和边缘计算迁移，遥感图像分割模型需满足严格的延迟与功耗约束。本文提出SatReg，一种基于回归的硬件感知调优框架，用于边缘平台上的轻量级遥感图像分割。以CM-UNet为教师模型，将搜索空间缩减为两个主要的宽度相关变量，在NVIDIA Jetson Orin Nano上对少量学生模型进行性能剖析，并拟合低阶代理模型以预测mIoU、延迟和功耗。通过知识蒸馏高效训练采样学生模型。所学代理模型可无需穷举搜索，快速选择接近最优的架构配置。结果表明，不同变量对任务精度与硬件开销影响各异，证明在简化空间内进行回归是适配混合CNN-Mamba分割模型至未来星地系统的有效策略。
原文摘要 · Abstract (English)
As Earth-observation workloads move toward onboard and edge processing, remote-sensing segmentation models must operate under tight latency and energy constraints. We present SatReg, a regression-based hardware-aware tuning framework for lightweight remote-sensing segmentation on edge platforms. Using CM-UNet as the teacher architecture, we reduce the search space to two dominant width-related variables, profile a small set of student models on an NVIDIA Jetson Orin Nano, and fit low-order surrogate models for mIoU, latency, and power. Knowledge distillation is used to efficiently train the sampled students. The learned surrogates enable fast selection of near-optimal architecture settings for deployment targets without exhaustive search. Results show that the selected variables affect task accuracy and hardware cost differently, making reduced-space regression a practical strategy for adapting hybrid CNN-Mamba segmentation models to future space-edge systems.
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