Camouflage　object　detection　aims　to　detect　highly　concealed　objects　hidden　in　complex　environments,　and　has　important　application　value　in　many　fields　such　as　medicine　and　agriculture.　The　existing　methods　that　combine　boundary　priors　excessively　emphasize　boundary　area　and　lack　the　ability　to　represent　the　internal　information　of　camouflaged　objects,　resulting　in　inaccurate　detection　of　the　internal　area　of　the　camouflaged　objects　by　the　model.　At　the　same　time,　existing　methods　lack　effective　mining　of　foreground　features　of　camouflaged　objects,　resulting　in　the　background　area　being　mistakenly　detected　as　camouflaged　object.　To　address　the　above　issues,　this　paper　proposes　a　camouflage　object　detection　method　based　on　boundary　feature　fusion　and　foreground　guidance,　which　consists　of　several　stages　such　as　feature　extraction,　boundary　feature　fusion,　backbone　feature　enhancement　and　prediction.　In　the　boundary　feature　fusion　stage,　the　boundary　features　are　first　obtained　through　the　boundary　feature　extraction　module　and　the　boundary　mask　is　predicted.　Then,　the　boundary　feature　fusion　module　effectively　fuses　the　boundary　features　and　boundary　mask　with　the　lowest　level　backbone　features,　thereby　enhancing　the　camouflage　object’s　boundary　position　and　internal　region　features.　In　addition,　a　foreground　guidance　module　is　designed　to　enhance　the　backbone　features　using　the　predicted　camouflage　object　mask.　The　camouflage　object　mask　predicted　by　the　previous　layer　of　features　is　used　as　the　foreground　attention　of　the　current　layer　features,　and　performing　spatial　interaction　on　the　features　to　enhance　the　network’s　ability　to　recognize　spatial　relationships,　thereby　enabling　the　network　to　focus　on　fine　and　complete　camouflage　object　areas.　A　large　number　of　experimental　results　in　this　paper　on　four　widely　used　benchmark　datasets　show　that　the　proposed　method　outperforms　the　19　mainstream　methods　compared,　and　has　stronger　robustness　and　generalization　ability　for　camouflage　object　detection　tasks.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.