The　most　prominent　features　of　fractal　dimension　analysis　are　high　sensitivity　to　damage　and　instant　determination　of　damage　location.　However,　an　intrinsic　deficiency　is　that　it　is　difficult　to　identify　damage　in　a　structure　while　suppressing　noise　simultaneously.　In　addition,　the　vibration　measurement　with　low-spatial-resolution　is　also　the　main　factor　restricting　the　accurate　damage　location.　In　this　paper,　a　novel　damage　detection　method　based　on　fractal　dimension　is　proposed,　in　order　to　provide　the　high-spatial-resolution　vibration　information,　the　phase-based　optical　flow　is　introduced,　which　is　a　vision-based　measurement　method　that　can　calculate　the　structural　vibration　with　high-spatial-resolution.　Then,　to　detect　damage　in　a　structure　while　suppressing　noise,　an　advanced　signal　analysis　method　is　proposed,　with　this　method,　the　fractal　dimension　(FRAC)　is　mapped　into　Difference　of　Gaussian　(DOG)　multi-scale　space,　producing　the　FRAC-DOG　to　reveal　damage-induced　singularities　in　mode　shapes.　Finally,　a　data　fusion　technique　is　applied　to　the　proposed　method　to　provide　a　reasonable　damage　detection　result　that　maximises　the　elimination　of　uncertainty.　The　numerical　and　experimental　results　demonstrate　that　the　proposed　method　can　detect　damage　with　high-precision　in　noisy　environments　by　comparing　it　with　the　existing　damage　detection　methods.