Aerial image object detection faces significant challenges due to notable scale variations, numerous small objects, complex backgrounds, illumination variability, motion blur, and densely overlapping objects, placing stringent demands on both accuracy and real-time performance. Although Transformer-based real-time detection methods have achieved remarkable performance by effectively modeling global context, they typically emphasize non-local feature interactions while insufficiently utilizing high-frequency local details, which are crucial for detecting small objects in aerial images. To address these limitations, we propose a novel VMC-DETR framework designed to enhance the extraction and utilization of high-frequency texture features in aerial images. Specifically, our approach integrates three innovative modules: (1) the VHeat C2f module, which employs a frequency-domain heat conduction mechanism to fine-tune feature representations and significantly enhance high-frequency detail extraction; (2) the Multi-scale Feature Aggregation and Distribution Module (MFADM), which utilizes large convolution kernels of different sizes to robustly capture effective high-frequency features; and (3) the Context Attention Guided Fusion Module (CAGFM), which ensures precise and effective fusion of high-frequency contextual information across scales, substantially improving the detection accuracy of small objects. Extensive experiments and ablation studies on three public aerial image datasets validate that our proposed VMC-DETR framework effectively balances accuracy and computational efficiency, consistently outperforming state-of-the-art methods.

Deep neural networks for image denoising typically follow an encoder–decoder model, with convolutional (Conv) layers as essential components. Conv layers apply zero padding at the borders of input data to maintain consistent output dimensions. However, zero padding introduces ring-like artifacts at the borders of output images, referred to as border effects, which negatively impact the network’s ability to learn effective features. In traditional methods, these border effects, associated with convolutional/deconvolutional operations, have been mitigated using patch-based techniques. Inspired by this observation, patch-wise denoising algorithms were explored to derive a CNN architecture that avoids border effects. Specifically, we extend the patch-wise autoencoder to learn image mappings through patch extraction and patch-averaging operations, demonstrating that the patch-wise autoencoder is equivalent to a specific convolutional neural network (CNN) architecture, resulting in a novel residual block. This new residual block includes a mask that enhances the CNN’s ability to learn border features and eliminates border artifacts, referred to as the Border-Enhanced Residual Block (BERBlock). By stacking BERBlocks, we constructed a U-Net denoiser (BERUNet). Experiments on public datasets demonstrate that the proposed BERUNet achieves outstanding performance. The proposed network architecture is built on rigorous mathematical derivations, making its working mechanism highly interpretable. The code and all pretrained models are publicly available.