DCA-Net: Deep Context Aggregation Network for Remote Sensing Object Detection

Abstract

Object detection in remote sensing scenarios faces multiple challenges, primarily manifested in multi-scale objects, class imbalance, densely packed small targets, and occlusion. To address these issues, we propose DCA-Net, an object detection method based on deep context aggregation. Built upon the YOLO architecture, the proposed method introduces parallel multi-branch pooling paths and an adaptive feature reweighting mechanism to construct a deep context aggregation backbone (DCA-Backbone). This enables efficient fusion and selective enhancement of multi-scale contextual semantic information. Furthermore, a detection head based on efficient channel interaction (ECI-Head) is designed to enhance the representational capability for multi-scale objects and complex morphological features. Experimental results demonstrate that DCA-Net achieves mAP50:95 of 50.5% on DIOR dataset and 61.5% on NWPU VHR-10 dataset, achieving competitive performance compared with other methods. Extensive visualization and ablation experiments further validate its detection accuracy and robustness in complex remote sensing scenarios.