The　pedestrian　detection　network　utilizing　a　combination　of　infrared　and　visible　image　pairs　can　improve　detection　accuracy　by　fusing　their　complementary　information,　especially　in　challenging　illumination　conditions.　However,　most　existing　dual-modality　methods　only　focus　on　the　effectiveness　of　feature　maps　between　different　modalities　while　neglecting　the　issue　of　redundant　information　in　the　modalities.　This　oversight　often　affects　the　detection　performance　in　low　illumination　conditions.　This　paper　proposes　an　efficient　attention　feature　fusion　network　(EAFF-Net),　which　suppresses　redundant　information　and　enhances　the　fusion　of　features　from　dualmodality　images.　Firstly,　we　design　a　dual-backbone　network　based　on　CSPDarknet53　and　combine　with　an　efficient　partial　spatial　pyramid　pooling　module　(EPSPPM),　improving　the　efficiency　of　feature　extraction　in　different　modalities.　Secondly,　a　feature　attention　fusion　module　(FAFM)　is　built　to　adaptively　weaken　modal　redundant　information　to　improve　the　fusion　effect　of　features.　Finally,　a　deep　attention　pyramid　module　(DAPM)　is　proposed　to　cascade　multi-scale　feature　information　and　obtain　more　detailed　features　of　small　targets.　The　effectiveness　of　EAFF-Net　in　pedestrian　detection　has　been　demonstrated　through　experiments　conducted　on　two　public　datasets.