Aiming at the problem of low detection accuracy of small targets in remote sensing images due to complex background, small size and dense arrangement, a remote sensing small target detection method integrating receptive field amplification and feature enhancement is proposed. Using YOLOv8s as the baseline network, the method firstly constructs a receptive field amplification module for the feature extraction part of the backbone network, and efficiently captures the global feature information through the Bi-Level Routing Attention (BRA); secondly, it constructs a shallow feature fusion structure in the feature pyramid part, and adds the improved coordinate spatial attention (CSA) in the transverse connectivity part of the shallow feature map, in order to enhance the feature information of the small targets; Finally, the detection results are post-processed by an improved non-maximum suppression (NMS) algorithm to adapt to the detection of objects with different densities Experiments are carried out on the DIOR remote sensing image dataset, the mean average precision (mAP) reaches 90.3% when the intersection and concurrency ratio threshold (IoU) between the predicted frame and the real frame is 0.5, which is 3% higher than that of the original model; and the mAP reaches 71.3% when the IoU is 0.5∶0.95, which is 6.1% higher than that of the original model, and the experimental results show that the improved model has a good application value for the small target detection in remote sensing images.