During　the　operating　lifecycle　of　civil　structures,　cracks　will　occur　inevitably,　which　may　pose　great　threat　to　the　safety　of　the　structures　without　timely　maintenance.　Digital　image　processing　techniques　have　great　potential　in　automatically　detecting　cracks,　which　can　replace　the　labor-intensive　and　highly　subjective　traditional　manual　on-site　inspections.　Therefore,　this　paper　presents　a　crack　detection　technology　based　on　a　convolutional　neural　network,　GoogLeNet　Inception　V3.　Firstly,　a　crack　image　dataset　is　acquired　and　constructed,　which　includes　2682　images　with　cracks　and　983　images　without　crack　at　a　resolution　of　256　x　256　pixels.　Then,　based　on　a　transfer　learning　method,　the　pretrained　GoogLeNet　Inception　V3　model　is　retrained　by　the　crack　dataset　for　better　identifying　the　crack　images.　The　accuracy　of　the　final　trained　model　on　the　test　set　can　reach　0.985.　Moreover,　image　stitching　based　on　Oriented　FAST　and　Rotated　BRIEF　feature　matching　algorithm　is　realized,　in　order　to　overcome　the　limitation　of　camera　field　of　view.　Compared　with　the　traditional　image　processing　technology,　the　method　adopted　in　this　work　can　automatically　study　the　characteristics　of　the　object　from　the　dataset,　which　can　adapt　to　the　complex　real　environment.　Due　to　the　transfer　learning　method,　the　crack　detection　can　be　achieved　based　on　the　existing　well-trained　models　after　being　retrained　by　a　small　dataset.