Animal　detection　and　recognition　is　a　crucial　task　in　computer　vision.　YOLOv5　has　been　widely　used　for　animal　identification　in　the　past　few　years.　However,　it　is　still　a　challenging　task　due　to　the　diverse　array　of　animal　types　found　in　complex　environments.　In　this　paper,　we　introduce　a　new　attention　mechanism　based　on　the　CBAM　attention　mechanism　to　enhance　the　performance　of　the　network　model.　Specifically,　the　attention　mechanism　enhances　the　interplay　between　globally　pooled　channel　information,　thereby　bolstering　the　ability　to　detect　and　recognize　animals　with　similar　features　in　complex　backgrounds.　Experimental　results　on　the　Oxford-IIIT　Pet　validation　dataset　demonstrate　the　effectiveness　of　the　proposed　model＇s　robustness　and　its　ability　to　perform　effectively　in　real-world　scenarios.　©　2024　SPIE.