Diabetic　retinopathy　(DR)　is　the　most　serious　and　common　complication　of　diabetes.　Microaneurysm　(MA)　detection　is　of　great　importance　for　DR　screening　by　providing　the　earliest　indicator　of　presence　of　DR.　Extremely　small　size　of　MAs,　low　color　contrast　in　fundus　images,　and　the　interference　from　blood　vessels　and　other　lesions　with　similar　characteristics　make　MA　detection　still　challenging.　In　this　paper,　a　novel　two-stage　MA　detector　with　multiscale　attention　and　trident　Region　proposal　network　(RPN)　is　proposed.　A　scale　selection　pyramid　network　based　on　the　attention　mechanism　is　established　to　improve　detection　performance　on　the　small　objects　by　reducing　the　gradient　inconsistency　between　low　and　high　level　features.　Meanwhile,　a　trident　RPN　with　three-branch　parallel　feature　enhance　head　is　designed　to　promote　more　distinguishing　learning,　further　reducing　the　misrecognition.　The　proposed　method　is　validated　on　IDRiD,　e-ophtha,　and　ROC　datasets　with　the　average　scores　of　0.516,　0.646,　and　0.245,　respectively,　achieving　the　best　or　nearly　optimal　performance　compared　to　the　state-of-the-arts.　Besides,　the　proposed　MA　detector　illustrates　a　more　balanced　performance　on　the　three　datasets,　showing　strong　generalization.