DAG-YOLO: A Context-Feature Adaptive fusion Rotating Detection Network in Remote Sensing Images

Abstract

Object detection in remote sensing image (RSI) research has seen significant advancements, particularly with the advent of deep learning. However, challenges such as orientation, scale, aspect ratio variations, dense object distribution, and category imbalances remain. To address these challenges, we present DAG-YOLO, a one-stage context-feature adaptive weighted fusion network that incorporates through three innovative parts. First, we integrate 1D Gaussian Angle-coding with YOLOv5 to convert the angle regression task into a classification task, establishing a more robust rotating object detection baseline, GLR-YOLO. Second, we introduce the Dual Branch Context Adaptive Modeling module, which enhances feature extraction capabilities by capturing global context information. Third, we design an adaptive detect head with the Adaptive Global Feature Aggregation and Reweighting (AGFAR) module. AGFAR addresses feature inconsistency among different output layers of the Feature Pyramid Network, retaining useful semantic information and elevating detection accuracy. Extensive experiments on public datasets DOTA-v1.0, DOTA-v1.5, and UCAS-AOD showcase mAP scores of 77.75%, 73.79%, and 90.27%, respectively. Our proposed method has the best performance among the current mainstream SOTA methods, which proves its effectiveness in RSI object detection.