DGE-YOLO: Dual-Branch Gathering and Attention for Accurate UAV Object Detection
This work addresses robust object detection for UAV applications, but it is incremental as it builds on the YOLO framework with specific enhancements.
The paper tackled the challenge of detecting small objects in multi-modal UAV scenarios by proposing DGE-YOLO, which achieved superior performance over state-of-the-art methods on the Drone Vehicle dataset.
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. Existing approaches often prioritize inference speed, leading to degraded performance when handling multi-modal inputs. To address this, we present DGE-YOLO, an enhanced YOLO-based detection framework designed to effectively fuse multi-modal information. Specifically, 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 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.