GlimmerNet: 一种轻量级的分组稀疏深度卷积用于无人机应急监控
GlimmerNet: A Lightweight Grouped Dilated Depthwise Convolutions for UAV-Based Emergency Monitoring
AI总结：
GlimmerNet通过轻量级分组稀疏深度卷积实现高效多尺度特征提取，以提升无人机应急监控的准确率与效率。
摘要：
卷积神经网络（CNNs）由于其计算效率，在边缘和移动视觉任务中已证明非常有效。尽管许多最近的工作试图通过基于自注意力的视觉变换器增强CNNs以获得全局上下文理解，但这些方法通常会引入显著的计算开销。在本工作中，我们证明了可以在不依赖计算昂贵组件的情况下保留强大的全局感知能力。我们提出了GlimmerNet，一种基于将感受野多样性与特征重组分离原理构建的超轻量级卷积网络。GlimmerNet引入了分组稀疏深度卷积（GDBlocks），将通道分成具有不同扩张率的组，从而在不增加参数成本的情况下实现多尺度特征提取。为了高效融合这些特征，我们设计了一个新的聚合模块，利用分组点卷积重新组合跨组表示，显著降低了参数开销。仅使用31K参数和比最新基线少29%的FLOPs，GlimmerNet在针对无人机的AIDERv2数据集上实现了新的状态-of-the-art加权F1分数为0.966。这些结果为在资源受限的无人机平台上实时应急监控建立了新的准确率-效率权衡前沿。我们的实现已在https://github.com/djordjened92/gdd-cnn公开。
英文摘要：
Convolutional Neural Networks (CNNs) have proven highly effective for edge and mobile vision tasks due to their computational efficiency. While many recent works seek to enhance CNNs with global contextual understanding via self-attention-based Vision Transformers, these approaches often introduce significant computational overhead. In this work, we demonstrate that it is possible to retain strong global perception without relying on computationally expensive components. We present GlimmerNet, an ultra-lightweight convolutional network built on the principle of separating receptive field diversity from feature recombination. GlimmerNet introduces Grouped Dilated Depthwise Convolutions(GDBlocks), which partition channels into groups with distinct dilation rates, enabling multi-scale feature extraction at no additional parameter cost. To fuse these features efficiently, we design a novel Aggregator module that recombines cross-group representations using grouped pointwise convolution, significantly lowering parameter overhead. With just 31K parameters and 29% fewer FLOPs than the most recent baseline, GlimmerNet achieves a new state-of-the-art weighted F1-score of 0.966 on the UAV-focused AIDERv2 dataset. These results establish a new accuracy-efficiency trade-off frontier for real-time emergency monitoring on resource-constrained UAV platforms. Our implementation is publicly available at https://github.com/djordjened92/gdd-cnn.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
