轻量化遥感分类模型在边缘设备上实现高效低耗推理
Lightweight Remote Sensing Scene Classification on Edge Devices via Knowledge Distillation and Early-exit
- 用频域知识蒸馏压缩模型，再结合动态提前退出机制
- 推理速度提升1.3倍，能耗降低超40%，准确率仍高
- 适合资源受限的边缘遥感应用，如无人机、移动传感器
随着轻量级深度学习算法的发展，多种深度神经网络（DNN）模型已被提出用于遥感场景分类（RSSC）。然而，在资源受限的边缘设备上，这些模型在模型精度、推理延迟和能耗之间仍难以取得最优平衡。本文提出一种轻量级RSSC框架，包含一个经过知识蒸馏的全局滤波网络（GFNet）模型和专为边缘设备设计的动态提前退出机制，以实现领先性能。具体而言，我们首先在频域对GFNet模型进行知识蒸馏以减小模型尺寸；随后设计了针对边缘设备上DNN模型的动态提前退出结构，进一步提升推理效率。我们在三个边缘设备上，使用四个数据集对E3C模型进行了评估。实验结果表明，该模型平均推理速度提升1.3倍，能耗降低超过40%，同时保持了高分类准确率。
原文摘要 · Abstract (English)
As the development of lightweight deep learning algorithms, various deep neural network (DNN) models have been proposed for the remote sensing scene classification (RSSC) application. However, it is still challenging for these RSSC models to achieve optimal performance among model accuracy, inference latency, and energy consumption on resource-constrained edge devices. In this paper, we propose a lightweight RSSC framework, which includes a distilled global filter network (GFNet) model and an early-exit mechanism designed for edge devices to achieve state-of-the-art performance. Specifically, we first apply frequency domain distillation on the GFNet model to reduce model size. Then we design a dynamic early-exit model tailored for DNN models on edge devices to further improve model inference efficiency. We evaluate our E3C model on three edge devices across four datasets. Extensive experimental results show that it achieves an average of 1.3x speedup on model inference and over 40% improvement on energy efficiency, while maintaining high classification accuracy.
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