The　icing　of　transmission　lines　will　bring　considerable　challenges　to　the　safe　operation　of　the　power　grid.　Therefore,　a　novel　method　combines　machine　vision　and　machine　learning　algorithms　for　identifying　the　ice　thickness　on　high-voltage　transmission　line　(HVTL)　as　proposed　herein.　First,　noise　and　background　interference　in　the　image　are　filtered,　and　the　grey　image　is　used　as　input.　Then,　the　algorithms　of　improved　Canny　edge　detection,　Hough　transform,　improved　K-means　clustering,　and　least-squares　fitting　are　adopted　in　turn　to　locate　the　edges　of　conductors.　Finally,　according　to　the　distance　mapping　model　based　on　monocular　vision,　the　ice　thickness　of　the　conductor　is　determined　by　calculating　the　width　difference　before　and　after　icing.　The　experimental　results　show　that　the　proposed　method　can　accurately　locate　the　edge　of　the　conductor　in　both　field　and　experimental　environments.　Moreover,　it　can　ensure　ideal　effects　under　different　illumination　and　hardly　not　be　affected　by　distortion　in　both　horizontal　and　vertical　directions.　Besides,　the　distance　mapping　model　can　map　the　pixel　distance　to　the　actual　distance　with　high　precision,　no　matter　whether　the　background　is　simple　or　complex,　and　the　calculated　ice　thickness　has　only　a　small　deviation　compared　to　the　actual　value.　In　addition,　the　proposed　method　shows　high　reliability　and　effectiveness　when　various　interference　such　as　different　backgrounds,　uneven　icing,　height　difference　changes,　conductor　movement,　contrast　changes,　and　conductor　sag　occur.