改进YOLOv13的红外遥感小目标检测算法OA

To address the challenges of small object size,low signal-to-noise ratio,and susceptibility to complex back-ground 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 inte-grates 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.