To　solve　the　problem　of　the　low　accuracy　of　self-supervised　monocular　depth　estimation,　a　self-supervised　monocular　depth　estimation　method　based　on　dual-discriminator　generative　adversarial　networks　were　proposed　in　this　paper.　The　advantages　of　generative　adversarial　networks　were　adopted　to　synthesize　visually　credible　images,　and　further　improve　the　accuracy　of　self-supervised　monocular　depth　estimation.　First,　to　make　full　use　of　the　reconstructed　image,　the　Wasserstein　generative　adversarial　networks　were　improved　and　the　structure　of　two　discriminators　was　constructed.　The　dual-discriminator　had　more　stringent　requirements　for　the　generator　and　training　objectives,　avoiding　the　information　loss　caused　by　the　introduction　of　discriminator　only　on　the　left　image　or　right　image.　Second,　according　to　the　structure　of　the　network,　a　local-global　consistent　loss　function　was　proposed　to　ensure　the　authenticity　of　pixels　and　the　consistency　of　local-global　content.　Results　show　that　the　proposed　method　effectively　improves　the　accuracy　of　monocular　depth　estimation　and　has　better　performance　of　depth　estimation.　©　2022,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.