Objective Given the complex underwater environment the target boundary may be blurred or the appearance and shape of the underwater target may be non-rigidly deformed due to light refraction which makes underwater target detection difficult. A DCN-YOLOv5 underwater target detection method based on the SimAM attention mechanism was proposed. Methods Firstly the bi-directional feature pyramid network BiFPN used by YOLOv5 was used to extract and fuse feature information on multiple scales to improve the accuracy of target recognition. Secondly to address the variations in appearance and shape of underwater objects the CBS module in the C3 module was combined with the deformable convolution network DCN and the DBS module was proposed. The DBS module was used to form the D3 module and replace part of the C3 module to adapt to the changing appearance and shape of the underwater targets. At the same time the weighted attention mechanism was integrated to adaptively adjust the attention of the model and further improve the feature expression ability in complex scenes. Finally considering the fuzzy boundary of the target and to improve the target positioning accuracy the WIoU Wise-IoU loss function was used to replace the cross-entropy loss, which can better adapt to the characteristics of different target types and sizes and improve the robustness of the algorithm. Results Experimental results showed that DCN-YOLOv5 achieved an average precision mAP of 87. 57% outperforming YOLOv5 and other classical networks with an average identification time of only 24. 5 ms per image. Conclusion The experimental results demonstrate that the model significantly improves detection accuracy while ensuring real-time detection providing valuable insights for the practical use of underwater target detection.