目的 针对远距离红外飞机目标检测中存在的由于成像面积小、辐射强度较弱造成无法充分提取目标特征进而影响检测性能的问题，提出一种基于全局-局部上下文自适应加权融合(Adaptive Weighted Fusion of Global-Local Context，AWFGLC)机制的红外飞机目标检测算法。方法 基于全局-局部上下文自适应加权融合机制，沿着通道维度随机进行划分与重组，将输入特征图切分为两个特征图。一个特征图使用自注意力进行全局上下文建模，建立目标特征与背景特征之间的相关性，突出目标较显著的特征，使得检测算法更好地感知目标的全局特征。对另一特征图进行窗口划分并在每个窗口内进行最大池化和平均池化以突出目标局部特征，随后使用自注意力对池化特征图进行局部上下文建模，建立目标与其周围邻域的相关性，进一步增强目标特征较弱部分，使得检测算法更好地感知目标的局部特征。根据目标特点，利用可学习参数的自适应加权融合策略将全局上下文和局部上下文特征图进行聚合，得到包含较完整目标信息的特征图，增强检测算法对目标与背景的判别能力。结果 将全局-局部上下文自适应加权融合机制引入YOLOv7并对红外飞机目标进行检测，实验结果表明，提出算法在自制和公开红外飞机数据集的mAP50分别达到97.8%、88.7%，mAP50:95分别达到65.7%、61.2%。结论 本文所提出的红外飞机检测算法，优于经典的目标检测算法，能够有效实现红外飞机目标检测。

Objective Aiming at the problem that the target features cannot be fully extracted due to the small imaging area and weak radiation intensity in long-range infrared aircraft target detection, we propose an infrared aircraft target detection algorithm based on Adaptive Weighted Fusion of Global-Local Context (AWFGLC). The global context tends to focus on the overall distribution of the target, which can provide the detection algorithm with global information of the target in the image with strong radiation intensity and clear contours. The local context tends to focus on the local details of the target and the background information around the target, and can provide the detection algorithm with local information of targets with weak radiation intensity and blurred contours. Therefore, the global context and local context should be combined according to the target characteristics in practical applications. Method Based on the global-local context adaptive weighted fusion mechanism, the input feature map is randomly divided and reorganized along the channel dimensions to cut the input feature map into two feature maps. Compared with global context modeling and local context modeling for input feature maps based on a specific arrangement or simply dividing the input feature map into two feature maps, during iterative training, the arrangement of the input feature map can be diversely changed by randomly reorganizing it with different random numbers in each training round, and global context modeling and local context modeling can help the detection algorithm learn more diversified and comprehensive features by combining feature maps with different arrangements. The global context modeling and local context modeling of different combinations of feature maps can help the detection algorithm learn more diverse and comprehensive features. Meanwhile, in order to reduce the complexity of contextual modeling of input feature maps, the complexity of contextual modeling is reduced by half by dividing the input feature maps equally in the channel dimension and performing global contextual modeling and local contextual modeling respectively. A feature map is subjected to global context modeling using self-attention to establish the dependencies between each pixel in the feature map and other pixels, to establish the correlation between the target features and the background features, to highlight the more salient features of the target, and to enable the detection algorithm to better perceive the global features of the target. The other feature map is divided into windows and maximum pooling and average pooling are performed within each window to highlight the local features of the target, and then the pooled feature map is subjected to local contextual modeling using self-attention to establish the correlation between the target and its surrounding neighborhoods, which further enhances the weaker parts of the target features and enables the detection algorithm to better perceive the local features of the target. According to the target characteristics, the global context and local context feature maps are adaptively weighted and channel spliced using an adaptive weighted fusion strategy of learnable parameters, so that the learnable parameters are updated along with the optimizer under the guidance of minimizing the loss function of the target detection algorithm. Subsequently, feature maps containing more complete target information are obtained using convolution, batch normalization and activation functions to enhance the detection algorithm's ability to discriminate between target and background. The mechanism of global-local context-adaptive weighted fusion is incorporated into the YOLOv7 feature extraction network, and the context modeling is performed on the downsampled 4-fold feature maps and the downsampled 32-fold feature maps to make full use of the physical information in the shallow feature maps and the semantic information in the deeper feature maps, in order to improve the model's capability of extracting infrared aircraft target features. Result The experimental results show that the proposed AWFGLC mechanism outperforms the contextual mechanisms such as Global Context, Position Attention, and Local Transformer in terms of detection accuracy under the condition of increasing the number of parameters and computation in the homemade infrared aircraft dataset. The proposed AWFGLC mechanism is more inclined to learn the global features of the target. Compared with the YOLOv7 detection algorithm, which has the second best performance, the proposed AWFGLC-YOLO algorithm improves the mAP50 and mAP50:95 by 1.4% and 4.4%, respectively. In the publicly available dataset of weak aircraft target detection and tracking in infrared images in ground/air context, the proposed AWFGLC mechanism is more inclined to learn the local features of the target. Compared to the YOLOv8 detection algorithm, which has the second best performance, the proposed AWFGLC-YOLO algorithm improves mAP50 and mAP50:95 by 3.0% and 4.0%, respectively. Conclusion The infrared aircraft detection algorithm proposed in this paper is superior to classical target detection algorithms and can effectively achieve infrared aircraft target detection.