UAV infrared image small target detection is a key technology in the fields of reconnaissance and surveillance, search and rescue, and its reliability is challenged by problems such as small target size, weak feature expression, and complex background. To solve the above problems, this paper proposes a UAV infrared small target detection algorithm PGF-RTDETR based on multi-scale perception and adaptive screening strategy. Firstly, a polarized channel-gated unit is constructed in the neck network, and the polarized attention mechanism is used to guide multi-scale features for deep semantic fusion, so as to enhance the recognizability of small targets and effectively suppress background interference. Secondly, an adaptive convolution enhancement module is designed to enhance the selectivity and fineness of features by using the gating mechanism to improve the extraction efficiency of fine-grained features. Finally, a high-efficiency residual structure that integrates partial convolution and channel transformation is introduced into the backbone network, which improves the feature expression ability and effectively reduces the number of model parameters. Experimental results show that compared with the benchmark model RT-DETR-R18, PGF-RTDETR increases mAP50 and P by 2.6% and 6.6% on the HIT-UAV dataset, and reduces the number of model parameters and computation by 20.7% and 25%, respectively, and mAP50 increases by 0.6% on the FLIR dataset. The improved model improves the detection accuracy while maintaining a small amount of parameters and computational amount, and provides an effective solution for UAV infrared small target detection.