As one of the most important research directions in the field of computer vision, target detection has made great progress. However, in the field of remote sensing image processing, target detection still has the problem of insufficient feature extraction about the small targets and the low accuracy caused by large-scale difference. We propose a multiscale remote sensing image target detection method based on frequency attention and feature fusion enhancement to achieve better detection results. This method can effectively learn the features extracted by multifrequency components through the frequency channel attention network, realize the interaction of diversified information from various channels, and improve the representation ability of the network. A unique feature pyramid network is designed using interactive up-and-down sampling and skip connection, which sufficiently fuses the features, effectively captures multiscale context information, and improves the detection performance of the model. A feature enhancement network combining dilated convolution and residuals is designed to mine deeper semantic information. The experimental results show that the proposed method achieves higher detection accuracy on the two datasets NWPU VHR-10 and RSOD, when compared with some state-of-the-art target detection methods, and effectively improves the detection performance.