Seeing Clearly and Detecting Precisely: Perceptual Enhancement and Focus Calibration for Small-Object Detection

Small-object detection remains challenging due to limited pixel information, blurred boundaries, and weak semantic cues. Although recent advances in multiscale fusion and attention mechanisms have led to improved performance, existing methods still struggle to preserve high-frequency structural details and achieve precise localization-particularly in dense, cluttered, or low-resolution scenarios. These limitations are primarily caused by the loss of fine-grained features during downsampling and the absence of region-aware focus mechanisms. Inspired by the human visual strategy of "see clearly and detect precisely," we propose PEFC-Net, a novel framework that enhances both perceptual clarity and localization accuracy for small-object detection. To mitigate structural degradation, we introduce the hybrid structural perception (HSP) module, which jointly encodes spatial gradients and localized frequency components through wavelet-based decomposition and edge-aware refinement. To further improve region-level focus, we design the axis-aligned focus calibration (AAFC) module, which captures long-range directional context via axis-sensitive pooling and adaptively refines attention with shape-aware calibration. Extensive experiments on four challenging benchmarks-VisDrone-2019, TT100K, NWPU VHR-10, and DIOR-demonstrate that PEFC-Net consistently outperforms state-of-the-art methods, delivering robust performance under occlusion, dense distribution, and scale variation.