Aiming at the difficulties of model training

large amount of model parameters

and slow detection speed in the saliency detection method based on deep neural network models

this paper proposes a saliency detection method that integrates small deep generative models. The method uses the generative adversarial network as the framework

designs a discriminator network consisting of 11 convolution modules and 5 pool layers

as well as a small generator network that does not contain the pool layer

including only 15 convolution modules and 5 transposed convolution modules. Among them

the size of the small generator network is only 2.4M

and the amount of parameters is only about 670

000. A trained small generator is used for

saliency detection and fused with the LMB (Salient object detection based on background block re-selection method) algorithm through the designed fusion algorithm to get the final result. A large number of experiments and comparative analysis show that the proposed method has achieved significant improvements in both

F

value and MAE (Mean Absolute Error) value.