Feature　pyramids　are　widely　adopted　in　visual　detection　models　for　capturing　multiscale　features　of　objects.　However,　the　utilization　of　feature　pyramids　in　practical　object　detection　tasks　is　prone　to　complex　background　interference,　resulting　in　suboptimal　capture　of　discriminative　multiscale　foreground　semantic　features.　In　this　article,　a　foreground　capture　feature　pyramid　network　(FCFPN)　for　multiscale　object　detection　is　proposed,　to　address　the　problem　of　inadequate　feature　learning　in　complex　backgrounds.　FCFPN　consists　of　a　foreground　dual　attention　(FDA)　module　and　a　pathway　aggregation　(PA)　structure.　Specifically,　the　FDA　mechanism　activates　top–down　foreground　channel　responses　and　lateral　spatial　foreground　location　features,　so　that　channel　and　spatial　foreground　features　are　adequately　captured.　Then,　the　PA　module　adaptively　learns　the　fusion　weights　of　multiscale　features　at　different　levels　of　the　feature　pyramid,　which　enhances　the　complementarity　of　semantic　information　between　different　levels　of　the　foreground　feature　maps.　Since　the　fusion　weights　are　learned　adaptively　based　on　different　pyramid　levels,　the　detection　model　accordingly　retains　the　gained　information　of　feature　sizes　and　suppresses　the　conflicting　information.　The　evaluations　on　public　datasets　and　the　self-built　complex　background　dataset　demonstrate　that　the　detection　average　precision　(AP)　and　the　feature　learning　performance　of　the　proposed　method　are　superior　compared　with　other　FPNs,　which　proves　the　effectiveness　of　the　proposed　FCFPN.　IEEE