关键词: 红外遥感, YOLOv13, 小目标检测, 跨尺度, 特征融合, Transformer

Abstract: To address the challenges of small object size, low signal-to-noise ratio, and susceptibility to complex background interference in infrared remote sensing, the YOLOv13 model is improved to meet the requirements of real-time and lightweight detection. A multi-level feature aggregation module (MFAM) is constructed to aggregate hierarchical information from different semantic depths and spatial resolutions in a bottom-up manner, while adaptively recalibrating their contributions to alleviate the dilution of small objects in deep semantic layers. A dual-path fusion pyramid network (DFPN) is designed, where a top-down semantic enhancement path and a bottom-up detail refinement path interact in a complementary manner to achieve cross-scale information circulation, thereby enhancing the separability of weak thermal targets. The proposed context-aware fusion block (CAFBlock) adopts a dual-branch structure of global self-attention and local depthwise convolution to jointly model long-range dependencies and fine-grained local features. In addition, it integrates a dual-path processing strategy of dilated convolution with multiple receptive fields and depthwise convolution for local details, combined with a gated fusion mechanism, to comprehensively strengthen multi-scale context modeling. Comparative experiments on the SIRST and HIT-UAV datasets demonstrate that the improved model achieves 90.06% and 64.37% AP, with relative gains of 7.65 percentage points and 8.55 percentage points, respectively, which verifies the effectiveness and feasibility of the proposed approach.

Key words: infrared remote sensing, YOLOv13, small object detection, cross-scale, feature fusion, Transformer