With the rapid development of mobile self-assembling network technology, cooperative control technology, sensing and detection technology, and artificial intelligence technology, unmanned aerial vehicle (UAV) group have gradually shown the characteristics of group intelligence distributed, self-organized and non-cooperative. Timely detection of an attacking UAV group allows for a wealth of countermeasures to be taken effectively. Countermeasures such as navigation deception, physical capture and physical destruction can be taken for a small number of UAV group, but once a large number of UAVs gather to form a UAV group, it is difficult to carry out countermeasures. Therefore, the development of UAV group target detection and identification technology is a prerequisite and key to achieving anti-UAV battlefield situational awareness. The existing target detection algorithms that do not consider the interrelationship between UAV group members, are prone to miss detection, mis-detect group members and fail to sense the structural characteristics of UAV group, we propose a method to sense the structural characteristics of UAV group based on infrared detection. Based on infrared detection and YOLOv5 algorithm, we propose an algorithm for sensing the structural characteristics of UAV group based on infrared detection, called GMR-YOLOv5 algorithm. The algorithm is designed by fusing the Space-to-Depth Non-strided Convolution (SPD-Conv) module with the Channel Attention Net (CAN) module to design the Space to Depth-Channel Attention Net (SD-CAN) module. The SPD-Conv module can convert the UAV features from the spatial dimension to the channel dimension, compared with the channel attention mechanism, which does not focus on the correlation between channels, and the designed SD-CAN module can realize the conversion of target features from the spatial dimension to the channel dimension, and also focus on the UAV features in the channel. Meanwhile, for the problem that the texture features of the UAV group members are not obvious in the infrared images, the Group Members relation (GMR) is constructed. This module makes full use of the structural information of UAV group members such as their positions and bounding box sizes in the infrared image, and incorporates the structural information of UAV group members into the association information between group members. Compared with the existing target detection algorithms, the proposed group membership relationship module in this paper considers the information such as the position and bounding box size of UAV group members in the image. Finally, the two constructed modules are fused to the YOLOv5 base network. The algorithm validation experiments are carried out on the self-built UAV group dataset. Experimental validation was carried out on the constructed Drone-swarms Dataset (Tab.1, Fig.4), and the experimental results showed that the mAP of the GMR-YOLOv5 algorithm proposed in the paper reached 95.9%, which improved the mAP of the original YOLOv5 algorithm by about 7%, effectively improving the detection accuracy of UAV group members (Tab.4). Meanwhile, the detection speed reached 59 FPS, which achieves real-time detection of UAV group targets and perception of UAV group structure characteristics. Compared with the classical detection algorithm, the GMR-YOLOv5 algorithm reduces the cases of missed and false detection of UAV targets (Fig.5-Fig.9). Ablation experiments are also conducted to demonstrate the effectiveness of each part of the improved module. The experimental results show that the proposed algorithm in the paper, although the detection speed is reduced, it has different degrees of improvement in the indexes mAP@0.50, mAP@0.50:0.95 (Tab.5). We propose an algorithm for sensing the structural characteristics of UAV group based on infrared detection. Firstly, the SPD-Conv module and the CAN module are combined to build a SD-CAN module, which not only converts drone features from the spatial dimension to the channel dimension, but also uses the channel attention mechanism to make the network pay more attention to the features of group in the channel, which improves the detection network's feature extraction ability for UAV group members. Secondly, using the position of group members in infrared image, boundary frame size and other structural information, the proposed GMR module, which generates connections among UAV group members, and then improves the detection and localization ability of the network for UAV group members. Meanwhile, the SIoU loss function is used to accelerate the convergence of the network. Finally, experimental validation is carried out on the UAV group dataset, and finally a network model with mAP of 95.9% and detection speed of 59 FPS is obtained to achieve UAV group structure characteristic sensing.