DGE-YOLO: Dual-Branch Gathering and Attention for Accurate UAV Object Detection

TL;DR

DGE-YOLO is a novel multi-modal UAV object detection framework that combines dual-branch architecture, multi-scale attention, and a gather-and-distribute module to improve detection accuracy, especially for small objects in complex aerial scenarios.

Contribution

The paper introduces DGE-YOLO, a new YOLO-based model with dual-branch fusion, EMA attention, and GD modules for enhanced multi-modal UAV object detection.

Findings

Outperforms state-of-the-art methods on Drone Vehicle dataset

Effectively detects small objects in complex aerial environments

Enhances feature learning through multi-scale attention and improved feature aggregation

Abstract

The rapid proliferation of unmanned aerial vehicles (UAVs) has highlighted the importance of robust and efficient object detection in diverse aerial scenarios. Detecting small objects under complex conditions, however, remains a significant challenge.To address this, we present DGE-YOLO, an enhanced YOLO-based detection framework designed to effectively fuse multi-modal information. We introduce a dual-branch architecture for modality-specific feature extraction, enabling the model to process both infrared and visible images. To further enrich semantic representation, we propose an Efficient Multi-scale Attention (EMA) mechanism that enhances feature learning across spatial scales. Additionally, we replace the conventional neck with a Gather-and-Distribute(GD) module to mitigate information loss during feature aggregation. Extensive experiments on the Drone Vehicle dataset demonstrate that DGE-YOLO achieves superior performance over state-of-the-art methods, validating its effectiveness in multi-modal UAV object detection tasks.