The　dropper　plays　a　critical　role　in　the　overhead　contact　system　(OCS)　of　high-speed　railways,　ensuring　smooth　power　transmission　and　reducing　vibration　between　the　contact　and　messenger　wires.　However,　adverse　factors,　such　as　temperature　variations,　inclement　weather,　and　high-frequency　vibrations　can　lead　to　dropper　loosening　and　detachment,　which　deteriorates　the　collecting　current　through　the　pantograph.　In　severe　cases,　it　can　even　result　in　pantograph　breakage　or　contact　wire　damage,　ultimately　causing　train　malfunctions.　Unfortunately,　existing　detection　methods　fall　short　in　recognizing　dropper　defects　in　real-world　scenarios.　To　address　this　challenge,　we　propose　a　novel　cross-fusion　of　convolutional　neural　network　and　transformer　for　high-speed　railway　dropper　defect　detection　(C2T-HR3D)　network.　Leveraging　a　cross-fusion　of　convolutional　neural　network　(CNN)　and　transformers,　this　network　accurately　recognizes　dropper　defects　in　challenging　scenarios,　such　as　fog,　rain,　sun,　and　night-time　conditions.　Moreover,　it　can　also　accurately　identify　obscured　and　small　dropper　defects　from　a　long　distance,　significantly　improving　recall　and　precision.　Extensive　experiments　have　demonstrated　that　our　network　outperforms　CNN-based,　transformer-based,　and　CNN-transformer　state-of-the-art　networks　by　3.4%,　1.8%,　and　2.1%,　respectively.　The　C2T-HR3D　network　has　been　successfully　deployed　on　over　300　high-speed　trains,　detecting　more　than　10000　dropper　defects.