Underwater fish images are affected by ligt scattering and absorption ,water impurities and other factors,resulting in low underwater fish image qua lity.This article uses improved automatic color equalization (ACE) algorithm to enhance underwater fish images to effectively impr ove image quality,and lay a good foundation for the subsequent underwater imag e segmentation.Aiming at the problems of poor segmentation effect and low real - time performance of underwater fish images,this paper proposes the ARD-PSPNet n etwork model,using the ResNet101 network model as the feature extraction networ k,and using the pyramid scene parsing network (PSPNet) network model with good segmentation performance as the basic image The segmentation model reduces the a mount of calculation by introducing deep separable convolutions.Through the R- M CN network structure,it makes full use of the rich location information and com pleteness of the shallow network feature layer,and improves the loss function t o make the segmentation position more accurate.In experiments and completed on the Fish4knowledge data set.E xperimental 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.