Visual　affordance　detection　aims　to　understand　the　functional　attributes　of　objects,　which　is　crucial　for　robots　to　achieve　interactive　tasks.　Most　existing　affordance　detection　methods　mainly　utilize　the　global　image　features　for　affordance　detection　while　do　not　fully　exploit　the　features　of　local　relevant　objects　in　the　image,　which　often　leads　to　suboptimal　detection　accuracy　under　the　interference　of　cluttered　backgrounds　and　neighbour　objects.　Numerous　researches　have　proved　that　the　accuracy　of　affordance　detection　largely　depends　on　the　quality　of　extracted　image　features.　In　this　paper,　we　propose　a　novel　affordance　detection　network　with　object　shape　mask　guided　feature　encoders.　The　masks　play　as　an　attention　mechanism　that　enforce　the　network　to　focus　on　the　shape　regions　of　target　objects　in　the　image,　which　facilitate　to　obtain　high-quality　features.　Specifically,　we　first　propose　a　shape　mask　guided　encoder,　which　uses　masks　to　effectively　locate　all　target　objects　so　as　to　extract　more　expressive　features.　Based　on　the　encoder,　we　then　propose　a　dual　enhance　feature　aggregation　module,　which　consists　of　two　branches.　The　first　branch　encodes　the　global　features　of　the　original　image,　while　the　second　branch　locates　each　local　relevant　object　and　encodes　its　precise　features.　Aggregating　these　features　enhances　the　feature　representation　of　each　object,　further　improving　feature　quality　and　suppressing　interference.　Quantitative　and　qualitative　evaluations　compared　with　state-of-the-art　methods　demonstrate　that　the　proposed　method　achieves　superior　performance　on　the　two　commonly　used　affordance　detection　datasets.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.