Vibration　measurement　serves　as　the　basis　for　various　engineering　practices　such　as　natural　frequency　or　resonant　frequency　estimation.　As　image　acquisition　devices　become　cheaper　and　faster,　vibration　measurement　and　frequency　estimation　through　image　sequence　analysis　continue　to　receive　increasing　attention.　In　the　conventional　photogrammetry　and　optical　methods　of　frequency　measurement,　vibration　signals　are　first　extracted　before　implementing　the　vibration　frequency　analysis　algorithm.　In　this　work,　we　demonstrate　that　frequency　prediction　can　be　achieved　using　a　single　feed-forward　convolutional　neural　network.　The　proposed　method　is　verified　using　a　vibration　signal　generator　and　excitation　system,　and　the　result　compared　with　that　of　an　industrial　contact　vibrometer　in　a　real　application.　Our　experimental　results　demonstrate　that　the　proposed　method　can　achieve　acceptable　prediction　accuracy　even　in　unfavorable　field　conditions.