The　early　fault　detection　of　anti-bird　thorns　on　electrical　towers　is　of　great　significance　for　reducing　the　occurrence　of　bird-damages　and　ensuring　the　safe　and　reliable　operation　of　the　transmission　lines.　The　anti-bird　thorns　in　the　electrical　inspection　images　have　the　features　of　being　unnoticeable　in　contour　and　partially　overlapped　in　distribution,　which　poses　challenges　to　the　research　of　anti-bird　thorn　identification　and　fault　detection.　Aiming　at　the　characteristics　of　the　anti-bird　thorns,　we　propose　a　component　identification　and　fault　detection　method　based　on　deep　convolution　neural　network.　First,　an　electrical　inspection　image　is　sharpened　by　the　sharpening　filter.　Then,　the　region　of　an　anti-bird　thorn　that　is　processed　by　the　sharpening,　is　bounded　and　cropped　by　the　object　detection　network　YOLOv3　which　is　trained　with　multi-scaling.　Finally,　the　anti-bird　thorn　fault　detector　based　on　the　feature　extraction　network　Resnet152　is　utilized　to　process　the　cropped　area　of　the　anti-bird　thorn,　realizing　the　fault　detection.　The　proposed　method　is　tested　on　the　electrical　inspection　images　of　the　validation　dataset　for　component　identification　and　fault　detection　of　the　anti-bird　thorn　with　the　average　precision　of　95.36%　and　92.3%　for　the　component　identification　and　the　fault　detection　respectively.　The　experimental　results　show　that　the　proposed　method　can　effectively　realize　the　component　identification　and　fault　detection　of　the　anti-bird　thorns　in　electrical　inspection　images.　©　2021,　Power　System　Technology　Press.　All　right　reserved.