CS n Net: A Remote Sensing Detection Network Breaking the Second-Order Limitation of Transformers With Recursive Convolutions

In recent years, transformer-based networks, known for their ability to model long-range dependencies, have been widely used in downstream computer vision tasks, surpassing certain neural network architectures. However, transformer-based networks suffer from issues such as large parameter size, high computational complexity, and difficulties in extending spatial and channel features to the third or even higher orders, resulting in convergence challenges for small to medium-sized datasets and limited effectiveness in extracting high-order detailed features. In this paper, we propose a high-order spatial and channel controllable convolution module, named CSn, which can replace standard convolutions in any convolutional network. In the context of remote sensing small object detection, CSndemonstrates superior performance compared to Self-Attention structures embedded in neural networks. Moreover, it introduces long-range dependency relationships among pixels, similar to Self-Attention, and adopts a cascaded recursive approach to extend spatial and channel features to arbitrary higher orders without introducing significant additional computation. This extension captures crucial information from high-order spatial and channel dimensions, resulting in improved accuracy for small object detection. Additionally, we construct a novel, versatile CSn-(FPN+PAN) structure for object detection networks, referred to as CSnNet. Finally, our proposed model exhibits significant advantages in remote sensing detection when compared to state-of-the-art methods on publicly available SAR datasets (SSDD, HRSID) and optical remote sensing dataset (NWPU-10), achieving respective improvements of 3.4%, 4.5%, and 0.4% in mAP50compared to baseline models.