：Aiming at the problem of low target contrast and multi-scale underwater images in underwater target detection, an underwater target detection algorithm based on the fusion of high-frequency enhanced network and Feature Pyramid Networks(FPN) is proposed. The algorithm improves the extraction of underwater target edge, contour information and target underlying information. Firstly, octave convolution is introduced to decompose the output features of the convolution layer by frequency, and the feature maps extracted by the backbone network are separated from high-frequency and low-frequency information. Since the contour information and noise information of underwater targets are contained in high-frequency features, Squeeze-and-Excitation Network with adaptive enhancement characteristics is introduced into the high-frequency information channel, and a high-frequency enhanced convolution is formed. It can achieve the purpose of enhancing useful contour feature information and suppressing noise. Secondly, the enhanced high-frequency feature components are integrated into the shallow network of FPN. It improves the feature representation ability of the original FPN for underwater multi-scale targets and alleviates the problem of missed detection of multi-scale targets. Finally, the proposed method is fused with the baseline algorithm Faster R-CNN, and the experiment is carried out on the dataset provided by the National Underwater Robot Competition. The results show that the recognition accuracy of the improved algorithm reaches 78.83%, which is 2.61% higher than the baseline. Compared with other types of target detection algorithms, it still has advantages of accuracy and real-time detection. The effectiveness of improving foreground and background contrast from the perspective of feature map frequency domain is demonstrated.