MITE-Net：面向嵌入式边缘搜救（SAR）的SWaP优化4K视频小目标感知
MITE-Net: SWaP-Optimized 4K Video Tiny Target Perception for Embodied Edge SAR
- University of Leicester（莱斯特大学）
- Guangzhou University（广州大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对嵌入式边缘搜救任务的SWaP约束，提出MITE-Net架构与SAR-Tiny数据集，在NVIDIA Jetson AGX Xavier上实现4K图像实时小目标感知，能效与召回率优于YOLO模型。
AI中文摘要：
高分辨率图像中的实时小目标感知对于嵌入式搜救（SAR）任务至关重要。然而，无人机等边缘设备严格的尺寸、重量和功耗（SWaP）约束形成了瓶颈：传统图像下采样会造成严重的特征损失，而基于切片的处理则会产生过高的延迟。为解决这一缺口，本文提出了一个涵盖新型架构、专用数据集和硬件级基准的综合框架。首先，我们提出了MITE-Net，这是一种SWaP优化的级联架构，它将受生物启发、无学习的基于小目标运动的区域提议网络（TTM-RPN）与参数小于0.14M的类R-CNN头部相结合。其次，为标准化4K小目标评估，我们通过重新标注两个具有挑战性的无人机数据集构建了SAR-Tiny数据集：SeaDroneSee-Tiny（动态海上场景，小目标主要为64-256像素）和UAVID-Tiny（杂乱城市场景，极小目标小于64像素）。第三，我们在边缘设备NVIDIA Jetson AGX Xavier上与最先进的YOLO模型进行基准测试，其中MITE-Net直接处理4K海上图像，以30.33 FPS达到100%的搜索成功率，仅消耗3.19 W（9.51 FPS/W），在目标召回率和能效方面远超YOLO基线。相反，UAVID-Tiny评估暴露了复合结构局限：无学习的仿生前端难以应对城市背景，而超轻量头部缺乏对复杂特征的表征能力。最终，本研究提供了一种高效的机载感知范式和严格的基准，为未来端到端SAR架构提供指导。
英文摘要：
Real-time tiny target perception in high-resolution imagery is critical for embodied Search-and-Rescue (SAR) missions. However, strict Size, Weight, and Power (SWaP) constraints on edge devices like UAVs create a bottleneck: traditional image downsampling causes severe feature loss, while slice-based processing incurs prohibitive latency. To address this gap, this paper introduces a comprehensive framework encompassing a novel architecture, specialized datasets, and hardware-level benchmarks. First, we propose MITE-Net, a SWaP-optimized cascaded architecture, which couples a bio-inspired, learning-free Tiny Target Motion-Based Region Proposal Network (TTM-RPN) with a sub-0.14M-parameter R-CNN-like head. Second, to standardize 4K tiny target evaluation, we construct the SAR-Tiny Datasets by relabeling two challenging UAV datasets: SeaDroneSee-Tiny (dynamic maritime scenes, tiny targets predominantly of 64-256 pixels ) and UAVID-Tiny (cluttered urban scenes, extremely tiny targets, less than 64 pixels). Third, we benchmark against state-of-the-art YOLO models on an edge device, NVIDIA Jetson AGX Xavier, where MITE-Net directly processes 4K maritime imagery, achieving a 100\% search success rate at 30.33 FPS. Consuming merely 3.19 W (9.51 FPS/W), MITE-Net vastly outperforms YOLO baselines in target recall and energy efficiency. Conversely, UAVID-Tiny evaluations expose a compound structural limitation: the learning-free bionic front-end struggles against urban backgrounds, while the ultra-lightweight head lacks representational capacity for complex features. Ultimately, this work delivers an efficient onboard perception paradigm and a rigorous baseline guiding future end-to-end SAR architectures.
