GlimmerNet: A Lightweight Grouped Dilated Depthwise Convolutions for UAV-Based Emergency Monitoring

TL;DR

GlimmerNet is a highly efficient CNN designed for UAV emergency monitoring, utilizing grouped dilated depthwise convolutions and a novel feature fusion method to achieve high accuracy with minimal parameters and computational cost.

Contribution

The paper introduces GlimmerNet, a lightweight CNN architecture that separates receptive field diversity from feature recombination, enabling multi-scale feature extraction without increasing parameters.

Findings

Achieves 0.966 weighted F1-score on UAV AIDERv2 dataset.

Uses only 31K parameters and 29% fewer FLOPs than recent baselines.

Demonstrates superior accuracy-efficiency trade-off for real-time UAV applications.

Abstract

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.