DCP-Net: Learning Detail–Context Perception via Spatial-Frequency Guidance for Tiny Object Detection in Remote Sensing Images

Tiny object detection (TOD) in remote sensing images has long been challenging due to the low resolution of tiny objects and complex backgrounds. However, existing adaptive receptive field methods mainly focus on object and contextual information, making them susceptible to interference from complex backgrounds. This interference exacerbates the imbalance in sample assignment and hinders accurate adaptation of receptive fields to object scales, resulting in insufficient representation of fine-grained object details. In addition, most loss functions are prone to gradient instability in scenarios involving small objects. To address the aforementioned problems, this article proposes a detail–context perception network (DCP-Net), which learns detail–context perception under spatial-frequency guidance. To enhance the representation of details of tiny objects, the network is the first to leverage spatial frequency to distinguish the structural features of objects and background. By introducing a spatial-frequency guided dual-dilated convolution (SpatialFGDDConv), it effectively enhances local object details and global semantic associations while suppressing interference from complex backgrounds. Based on this convolutional unit, we further design a cross-layer fine-grained fusion module to recover lost object details at a lower computational cost. Furthermore, we introduce a Gaussian classification-regression loss to address the issues of imbalanced sample allocation and unstable gradient, while applying a decoupled probabilistic metric strategy and a multiscale enhanced decoupled head (ME-Decoupled Head) to improve localization accuracy. Extensive experimental results demonstrate the effectiveness of the proposed method. Specifically, DCP-Net achieves an average precision (AP) of 27.5% on the AI-TODv2 dataset and an AP50 of 78.0% on the DOTA-v1.0 dataset. The code will be found at: https://github.com/yangyvni/DCP-Net.