Remote sensing image target detection is one of great significance in military reconnaissance, intelligent agriculture and other fields, especially small target detection has been gaining continuous attention. However, small targets in remote sensing images face the problems of insufficient feature information and difficult detection, which have become the biggest obstacles plaguing the development of remote sensing applications. To this end, the you only look once-hybrid feature(YOLO-HF) algorithm was proposed, which introduced a hybrid attention mechanism of channel attention and self-attention in the network of the traditional YOLOv7 model to extract the target's deep features, and fused the shallow and deep features to increase the richness of local features; to further strengthen the attention to the global information, a global attention mechanism was added for the small-scale targets after the extraction of the features, to achieve the ability of global feature expression enhancement. In order to avoid that the traditional loss function was sensitive to the positional deviation of small targets, which leaded to poor detection effect, a new metric was selected for use, which was embedded into the computation of the bounding box loss function, so as to accelerated the convergence of the loss function and realized the enhancement of the detection accuracy of small targets. The experimental results show that compared with the traditional YOLOv7 algorithm, the proposed algorithm shows superiority on both RSOD and NWPU VHR-10 datasets, and in particular, the mean average accuracy on RSOD dataset is improved by 2.90%, and the mean average accuracy on NWPU VHR-10 dataset realizes an improvement of 3.61%.