To address the technical bottlenecks of poor detection accuracy and robustness in small object detection caused by insufficient feature information and low-resolution feature maps, this paper proposes a small object detection method based on high-resolution feature-guided learning. The method adopts an improved YOLOv11 algorithm structure based on context aggregation pinwheel convolution and constructs a dual-channel detection framework consisting of a high-resolution detection branch and a low-resolution detection branch. During the training process, the high-resolution detection network guides the learning of the low-resolution detection network, alleviating the problem of insufficient semantic information of small objects in low-resolution images. A multi-scale feature alignment loss function with weighted multi-loss functions is introduced in the middle layer of the dual-channel network to enhance the expressive ability of small object features. Experimental results show that the proposed method achieves a 4.11% improvement in mAP50 and a 4.07% improvement in mAP50:95 compared to the original YOLOv11 on the PASCAL VOC 2012 small object dataset; on the Visdrone2019 dataset, the mAP50 increases by 2.24% and the mAP50:95 increases by 1.50% compared to the original YOLOv11.