Feature-Differentiated Perception with Dynamic Mixed Convolution and Spatial Orthogonal Attention for Faster Aerial Object Detection

In the field of remote sensing (RS) object detection, efficient and accurate target recognition is crucial for applications such as national defense and maritime monitoring. However, existing detection methods either have high computational complexity, making them unsuitable for real-time applications, or suffer from feature redundancy issues that affect detection accuracy. To address these challenges, this paper proposes a Feature-Differentiated Perception (FDP) lightweight remote sensing object detection method, which optimizes computational efficiency while maintaining high detection accuracy. The proposed method introduces two critical innovations: (1) Dynamic mixed convolution (DM-Conv), which uses linear mapping to efficiently generate redundant feature maps, reducing convolutional computation. It combines features from different intermediate layers through weighted fusion, effectively reducing the number of channels and improving feature utilization. Channel refers to a single feature map in the multi-dimensional feature representation, where each channel corresponds to a specific feature pattern (e.g., edges, textures, or semantic information) learned by the network. (2) The Spatial Orthogonal Attention (SOA) mechanism, which enhances the ability to model long-range dependencies between distant pixels, thereby improving feature representation capability. Experiments on public remote sensing object detection datasets, including DOTA, HRSC2016, and UCMerced-LandUse, demonstrate that the proposed model achieves a significant reduction in computational complexity while maintaining nearly lossless detection accuracy. On the DOTA dataset, the proposed method achieves an mAP (mean Average Precision) of 79.37%, outperforming existing lightweight models in terms of both speed and accuracy. This study provides new insights and practical solutions for efficient remote sensing object detection in embedded and edge computing environments.