Most　of　the　bridge　structures　in　the　world　are　built　of　reinforced　concrete.　With　the　growth　of　service　life　and　the　increase　of　urban　traffic　and　other　factors,　most　bridges　put　into　service　have　more　or　less　damage.　Traditional　bridge　damage　detection　methods　include　the　manual　inspection　method　and　bridge　inspection　vehicle　method,　which　have　many　shortcomings.　Moreover,　the　detection　of　cracks　in　bridges　is　critical　to　the　safety　of　transportation　due　to　the　extremely　large　number　of　bridges　built　in　the　road　networks　across　the　world.　To　this　end,　this　paper　uses　the　most　widely　used　CNN　in　deep　learning　to　identify　and　classify　crack　images　and　proposes　a　migration　learning　technique　to　solve　the　problem　of　the　large　amount　of　training　data　required　for　training　CNN.　The　data　augmentation　and　sliding　window　techniques　are　introduced　to　divide　the　collected　crack　data　into　training　establish　and　test　set.　The　experiments　show　that　the　method　in　this　paper　can　classify　the　crack　images　better,　extract　and　locate　the　cracks　of　bridge　crack　units,　and　finally　extract　the　crack　coordinates　of　boxing.　Compared　with　the　customary　image　recognition　methods,　the　method　used　in　this　paper　is　easier　to　operate　in　practical　engineering,　and　the　accuracy　of　the　obtained　results　is　higher.