-　The　identification　and　protection　of　wild　animals　are　crucial　to　maintaining　biodiversity　and　maintaining　ecological　balance.　Based　on　the　deep　learning　method　and　the　characteristics　of　field　monitoring　images,　this　paper　proposes　a　Dc_YOLOv5　(Deformable　convolution　YOLOv5)　and　NLBP　dual-pass　fusion　network　as　a　feature　extraction　module.　First,　due　to　the　randomness　of　animal　activities,　the　monitoring　images　may　have　complex　background　information.　In　order　to　improve　the　feature　expression　ability　of　complex　image　target　detection,　a　deformable　convolutional　neural　network　is　introduced　in　the　CSP2_1　module　of　the　YOLOv5　network;　in　the　LBP　pathway,　the　proposed　Compared　with　the　traditional　LBP　method,　the　NLBP　extraction　algorithm　takes　into　account　the　changing　relationship　between　the　gray　value　of　the　pixel　and　its　neighboring　pixels,　reducing　the　loss　of　feature　information;　finally,　the　CAFF-feature　fusion　module　is　introduced　to　reconstruct　the　feature　and　Obtain　intermediate　predictions,　effectively　focusing　on　important　channels　and　suppressing　irrelevant　channels.　The　experimental　results　show　that　this　method　has　excellent　recognition　effect.　©　2022　IEEE.