This　paper　proposes　a　non-contact　vibration　measurement　method　based　on　deep　learning　and　image　processing.　The　deep　learning　method　is　used　to　realize　the　automatic　and　efficient　selection　of　effective　pixels　and　the　optical　flow　method　is　used　to　extract　vibration　signals　to　realize　non-contact　and　targetless　visual　vibration　measurement.　In　this　study,　a　carbon　plate　board　and　aluminum　C-beam　structure　were　measured　and　verified　under　artificial　and　non-human　excitation　in　a　laboratory　environment.　Additionally,　bridge　and　cable　structures　in　an　outdoor　environment　were　selected　as　measurement　targets　to　verify　the　reliability　of　the　proposed　method.　This　paper　compares　the　experimental　results　of　Canny　and　Sobel　edge　detection　algorithms　and　deep　learning　methods　to　verify　the　efficiency　of　deep　learning.　The　results　demonstrate　that　our　method　is　robust,　even　under　real-world　unfavorable　conditions,　meaning　it　can　serve　as　a　novel　measurement　method　in　the　field　of　vibration　measurement.