ISTD-DINO:复杂环境下空间信息增强的红外小目标无人机检测算法OA

Although the applications of low-altitude small unmanned aerial vehicles(UAVs)are expanding,certain operational situations threaten safety.Infrared technology is widely used in anti-UAV systems because of its strong adaptability to complex environments.In this study,the problems of incomplete small-target feature usage and indistinct features in complex backgrounds in the infrared image detection task of UAVs were addressed,and the ISTD-DINO infrared UAV target detection model was proposed.The IFE feature enhancement method was proposed,and spatial and channel attention and multi-branch dilated convolution were adopted to fully mine the spatial information of infrared UAV target features;by fusing information at different levels,long-distance dependencies between multi-scale features were captured;the EIoU bounding box loss function was introduced to improve the detection accuracy of the model during training.Experimental results showed that the AP0.5 of the ISTD-DINO model reached 97.5%,indicating that ISTD-DINO has significant advantages in the infrared UAV target detection task.ISTD-DINO performed well in identifying small infrared targets across various complex scenarios,providing a technical solution with significant reference value for maintaining airspace order and the healthy development of the low-altitude economy.