DynaFilter：用于卫星边缘智能的云驱动动态过滤
DynaFilter: Cloud-driven Dynamic Filtering for Satellite Edge Intelligence
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中文总结 AI 辅助
针对卫星边缘系统带宽受限等问题，设计DynaFilter动态过滤技术，通过建立云查询语义与压缩域特征的映射，让边缘设备在压缩域直接进行选择性RoI推理，实现减少数据量、节省带宽、降低能耗及加快推理延迟等效果。
中文摘要 AI 辅助
现代卫星边缘系统，如执行目标检测和跟踪等遥感任务的系统，带宽严重受限且连接间歇性强，向云持续传输数据不切实际。现有边缘-云系统要么在分析前需大量预处理，如对图像数据完全解压，要么传输所有压缩数据而不顾相关性。为应对这些挑战，我们设计了DynaFilter，一种动态过滤技术，使卫星边缘设备能在压缩域直接进行选择性感兴趣区域（RoI）推理，无需完全解压。我们的关键见解是，低级压缩语法，特别是JPEG图像中的直流系数/交流能量和视频流中的运动向量，与高级语义查询有很强相关性。通过在云查询语义和多模态压缩域特征之间建立精确映射，DynaFilter使边缘能识别并仅传输与RoI相关的相关数据。广泛评估表明，与现有基线相比，DynaFilter对图像减少了解码和后续推理的像素数据总量1.6倍至7.1倍，视频流节省了92.0%的带宽。此外，它使目标设备能耗降低43.1%至88.6%，推理延迟加快1.6倍至3.0倍。
英文摘要
Modern satellite edge systems, including those performing remote sensing tasks such object detection and tracking, are characterized by severely limited bandwidth and intermittent connections, making continuous data transmission to the cloud impractical. Existing edge-cloud systems, however, either require heavy pre-processing before analysis, for instance, full decompression of imagery data, or transmit all compressed data regardless of relevance. To address these challenges, we design DynaFilter, a dynamic filtering technique that enables satellite edge devices to perform selective region-of-interest (RoI) inference directly in the compressed-domain, without full decompression. Our key insight is that low-level compression syntax, specifically DC coefficients/AC energy in JPEG images and motion vectors in video streams, exhibits strong correlations with high-level semantic queries. By establishing a precise mapping between cloud query semantics and multimodal compressed-domain features, DynaFilter enables the edge to identify and transmit only relevant data associated to RoIs. Extensive evaluations show that DynaFilter reduces the total volume of pixel data for decoding and subsequent inference by 1.6x-7.1x for images, and achieves 92.0% bandwidth savings for video streams compared to state-of-the-art baselines. Furthermore, it decreases energy consumption by 43.1-88.6% on target devices and achieves a 1.6x-3.0x speedup in inference latency.
发表机构
- Politecnico di Milano（米兰理工大学）
- Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。
