Feature Symmetry Fusion Remote Sensing Detection Network Based on Spatial Adaptive Selection

This paper proposes a spatially adaptive feature fine fusion network consisting of a Fast Convolution Decomposition Sequence (FCDS) and a Spatial Selection Mechanism (SSM). Firstly, in FCDS, a large kernel convolution decomposition operation is used to break down dense convolution kernels into small convolutions with gradually increasing hole rates, forming a continuous kernel sequence to obtain finer scale features. This approach significantly reduces the number of parameters, improves network inference efficiency, and preserves the spatial feature expression ability of the network. Notably, the decomposed convolution kernel sequence adopts a symmetric dilation rate increment strategy, maintaining symmetry constraints in kernel weight distribution while expanding receptive fields. On this basis, the spatial selection mechanism is utilized to enhance the key features and background differences of the target location in the feature map, dynamically allocate weights to different fine scale feature maps, and improve the adaptive ability of multi-scale domains. This mechanism employs symmetric attention weight allocation (symmetric channel attention + spatial attention) to establish complementary symmetric response patterns across feature maps in both channel and spatial dimensions. Numerous experiments have shown that our method achieves higher performance with 81.64%, 91.34%, 91.20%mAP on three commonly used remote sensing target datasets (DOTA, UCAS AOD, HRSC2016) compared to existing advanced detection networks.