A2-Det: Dual Asymmetric Architecture for Tiny Object Detection in Remote Sensing Imagery

To address the attenuation of shallow fine-grained structural information during deep feature propagation and the representational conflict between classification and regression in remote sensing tiny object detection, this paper proposes a dual asymmetric detection framework, termed A2-Det. Built upon YOLO11n, the detection pyramid is shifted toward the high-resolution P2–P4 levels by introducing a P2 detection branch and removing the original P5 stage, thereby reducing the loss of fine-grained spatial details caused by successive downsampling. On this high-resolution feature basis, a Query–Key–Value (QKV)-guided Asymmetric Spatial Feature Enhancement module (Q-ASFE) is deployed at the P2 and P3 stages, where direction-sensitive asymmetric convolutions are combined with lightweight QKV-based contextual modulation to strengthen tiny object structural responses while suppressing complex background interference. Furthermore, an Asymmetric Coordinate–Semantic Decoupled Head (ACS-Head) performs differentiated modeling for the semantic selection required by classification and the coordinate-sensitive representation required by regression, thereby alleviating task-specific representational conflict. Through the progressive coordination of high-resolution feature preservation, shallow-feature enhancement, and task-specific prediction, A2-Det improves mAP50 by 5.8 percentage points over YOLO11n on the VisDrone dataset. Consistent improvements on the USOD and RSOD datasets further demonstrate its effectiveness and cross-scene generalization.