Low　light　object　detection　is　a　challenging　problem　in　the　field　of　computer　vision　and　multimedia.　Most　available　object　detection　methods　are　not　accurate　enough　in　low　light　conditions.　The　main　idea　of　low　light　object　detection　is　to　add　an　image　enhancement　preprocessing　module　before　the　detection　network.　However,　the　traditional　image　enhancement　algorithms　may　cause　color　loss,　and　the　recent　deep　learning　methods　tend　to　take　up　too　many　computing　resources.　These　methods　are　not　suitable　for　low　light　object　detection.　We　propose　an　accurate　low　light　object　detection　method　based　on　pyramid　networks.　A　low-resolution　pyramid　enhancing　light　network　is　adopted　to　lessen　computing　and　memory　consumption.　A　super-resolution　network　based　on　attention　mechanism　is　designed　before　Efficientdet　to　improve　the　detection　accuracy.　Experiments　on　the　10K　RAW-RGB　low　light　image　dataset　show　the　effectiveness　of　the　proposed　method.