Underwater fish images are affected by ligt scattering and absorption,water impurities and other factors,resulting in low underwater fish image quality.This article uses improved automatic color equalization (ACE) algorithm to enhance underwater fish images to effectively improve image quality,and lay a good foundation for the subsequent underwater image segmentation.Aiming at the problems of poor segmentation effect and low real-time performance of underwater fish images,this paper proposes the ARD-PSPNet network model,using the ResNet101 network model as the feature extraction network,and using the pyramid scene parsing network (PSPNet) network model with good segmentation performance as the basic image The segmentation model reduces the amount of calculation by introducing deep separable convolutions.Through the R-MCN network structure,it makes full use of the rich location information and completeness of the shallow network feature layer,and improves the loss function to make the segmentation position more accurate.In experiments and completed on the Fish4knowledge data set.Experimental results show that the new model has an increase of 2.8% in mean intersection over union (MIOU) and about 2% in mean pixel accuracy (MPA) compared with the original model.