The　presence　of　the　bird　nests　on　the　electric　power　tower　becomes　a　hazard　to　the　safety　and　stability　of　the　transmission　line.　In　recent　years,　detecting　the　bird　nests　on　the　transmission　line　by　using　drones　is　being　one　of　the　essential　missions　of　power　inspection.　The　migration　of　image　processing　methods　from　computer　vision　to　power　image　identification　has　increasingly　becoming　a　trend.　The　detection　method　combining　Single　Shot　Detector　and　HSV　color　space　filter　is　proposed　in　this　paper　to　identify　the　bird　nests　by　making　use　of　the　image　features　with　a　large　color　span　under　different　illumination　angles.　The　fine-tuned　Single　Shot　Detector　network　is　trained　and　utilized　to　identify　the　bird　nests　and　the　detection　result　is　clipped　which　called　sub-images.　Then　the　sub-images　are　filtered　by　the　selector　based　on　HSV　color　space,　who　contains　none　object　of　bird　nests　can　be　removed　by　the　pixel　percentage.　The　experimental　results　show　that　the　proposed　method　can　accurately　detect　the　bird　nests　in　the　testing　transmission　line　inspection　images,　and　the　accuracy　can　be　up　to　98.23%.　Compared　with　other　single　traditional　methods,　the　proposed　bird　nests　detection　method　combining　the　deep　learning　and　the　HSV　color　space　filter　greatly　enhances　the　detection　accuracy.　©　2019　IEEE.