In　anchor-free　object　detection,　the　center　regions　of　bounding　boxes　are　often　highly　weighted　to　enhance　detection　quality.　However,　the　central　area　may　become　less　significant　in　some　situations.　In　this　paper,　we　propose　a　novel　dual　attention-based　approach　for　the　adaptive　weight　assignment　within　bounding　boxes.　The　proposed　improved　dual　attention　mechanism　allows　us　to　thoroughly　untie　spatial　and　channel　attention　and　resolve　the　confusion　issue,　thus　it　becomes　easier　to　obtain　the　proper　attention　weights.　Specifically,　we　build　an　end-to-end　network　consisting　of　backbone,　feature　pyramid,　adaptive　weight　assignment　based　on　dual　attention,　regression,　and　classification.　In　the　adaptive　weight　assignment　module　based　on　dual　attention,　a　parallel　framework　with　the　depthwise　convolution　for　spatial　attention　and　the　1D　convolution　for　channel　attention　is　applied.　The　depthwise　convolution,　instead　of　standard　convolution,　helps　prevent　the　interference　between　spatial　and　channel　attention.　The　1D　convolution,　instead　of　fully　connected　layer,　is　experimentally　proved　to　be　both　efficient　and　effective.　With　the　adaptive　and　proper　attention,　the　correctness　of　object　detection　can　be　further　improved.　On　public　MS-COCO　dataset,　our　approach　obtains　an　average　precision　of　52.7%,　achieving　a　great　increment　compared　with　other　anchor-free　object　detectors.