Abstract: An improved target detection algorithm for underwater sonar images is proposed. The proposed algorithm achieves excellent detection performance on underwater sonar images with high noise and incomplete target details. The image enhancement module GDIP, which is applied in extreme weather, is introduced and improved to SDIP (Sonar DIP) module, and the improved SDIP module is applied to sonar images with remarkable effect, which can adaptively adjust the parameters of the enhancement algorithm to improve the detection accuracy of the model. Meanwhile, a pixel-level enhancement threshold is also added to enhance the image details while suppressing noise amplification, which is also set as an optimizable parameter to be self-adaptively adjusted through model training. The detection model adopts the fusion structure of CNN and Transformer, and construts the more efficient and advanced Layer Normalization (LN) and Feed-forward Neural Network (FFN) structures in the Transformer network based on the DCNv3 operator. The internal architecture of the model is optimized, and EC, Hdc, and PC modules are added to enhance the performance of the model, which is based on the principle of lightweight deep convolution construction to capture local spatial information, and it has significant effect on underwater sonar images with large noise and serious information loss. The designed experiments prove that enhancing the acquisition of spatial information can effectively improve the detection performance of the model for this task.