The　technology　for　identifying　birds　around　power　towers　using　cameras　alone　is　still　susceptible　to　environmental　interference.　This　paper　proposes　a　new　bird　damage　recognition　network,　RCVNet,　which　addresses　this　issue　by　fusing　radio-frequency　(RF)　images　and　visual　images.　The　network　employs　a　feature　layer　fusion　approach　that　accurately　identifies　bird　damages　in　the　monitoring　area.　Initially,　RCVNet　takes　a　group　of　RF　and　visual　images　as　input.　Then,　through　a　series　of　convolutional　neural　networks　(CNNs),　birds　are　identified　and　located.　To　overcome　challenges　in　recognizing　small　targets,　several　improved　modules　such　as　crosssupervised　fusion　network　(CSF-net),　posture　deformable　convolution　(PDF),　small-target　attention　fusion　mechanism　(SAFM),　and　Tiny-YOLOHead　are　introduced　throughout　RCVNet,　improving　surface　information　utilization　and　small　feature　retention　rates.　Finally,　a　bird　damage　discrimination　strategy　is　developed　based　on　the　recognition　outcomes　of　birds.　As　there　is　currently　no　public　dataset　available　for　RCVNet　training,　a　new　bird　dataset　called　CRB2022,　which　includes　RF　and　visual　images,　was　gathered.　Through　large-scale　experiments　utilizing　these　methods,　RCVNet　effectively　identifies　birds,　achieving　a　mean　average　precision　of　79.34%　and　a　mean　average　recall　of　83.29%.　Additionally,　the　discrimination　rate　of　the　utilized　strategy　can　reach　up　to　98%.