Given　the　actual　needs　for　detecting　multiple　features　of　butterflies　in　natural　ecosystems,　this　paper　proposes　a　model　of　weakly-supervised　butterfly　detection　based　on　a　saliency　map　(WBD-SM)　to　enhance　the　accuracy　of　butterfly　detection　in　the　ecological　environment　as　well　as　to　overcome　the　difficulty　of　fine　annotation.　Our　proposed　model　first　extracts　the　features　of　different　scales　using　the　VGG16　without　the　fully　connected　layers　as　the　backbone　network.　Next,　the　saliency　maps　of　butterfly　images　are　extracted　using　the　deep　supervision　network　with　shortcut　connections　(DSS)　used　for　the　butterfly　target　location.　The　class　activation　maps　of　butterfly　images　are　derived　via　the　adversarial　complementary　learning　(ACoL)　network　for　butterfly　target　recognition.　Then,　the　saliency　and　class　activation　maps　are　post-processed　with　conditional　random　fields,　thereby　obtaining　the　refined　saliency　maps　of　butterfly　objects.　Finally,　the　locations　of　the　butterflies　are　acquired　based　on　the　saliency　maps.　Experimental　results　on　the　20　categories　of　butterfly　dataset　collected　in　this　paper　indicate　that　the　WBD-SM　achieves　a　higher　recognition　accuracy　than　that　of　the　VGG16　under　different　division　ratios.　At　the　same　time,　when　the　training　set　and　test　set　are　8:2,　our　WBD-SM　attains　a　95.67%　localization　accuracy,　which　is　9.37%　and　11.87%　higher　than　the　results　of　the　DSS　and　ACoL,　respectively.　Compared　with　three　state-of-the-art　fully-supervised　object　detection　networks,　RefineDet,　YOLOv3　and　single-shot　detection　(SSD),　the　detection　performance　of　our　WBD-SM　is　better　than　RefineDet,　and　YOLOv3,　and　is　almost　the　same　as　SSD.　©　2023　Elsevier　Ltd