Salient region detection has been widely applied in object recognition ,image segmentation,and image/video compression. It has an important topic in computer vision.However,the saliency detection me thods based on different visual salient features often cannot accurately detect salient objects,and they are also time-consuming. Recently,the convolutional neural networks (CNN)-based models have achieved remarkable success in image processing and image analysis.In orde r to enhance the detection performance of salient objects,this paper presents an image salient object detection approach based on conditional generative adversarial networks (cGAN).This method uses deep CNN to construct the cGAN.By iteratively fighting each other b etween the generator and the discriminator,the CNN can obtain salient features of images accurately and then urges the generator to produce the high-quality saliency maps.To further improve the saliency detection performance,the loss function of cGAN is defined by cooperating the GAN errors and the L1distance between the generator outputs and ground truth maps.Experimental results on the MSRA10K and ECSSD datasets show that the proposed approach outperforms state-of-the-art saliency detection models significantly ,and has superior performance with 94.19% and 96.24% in precision,93.99% and 90.13% in recall rate,and 94.15% and 94.76% in F-measure .