To　solve　the　problem　of　time-consuming　and　difficult　construction　of　image　datasets　in　bridge　engineering,　this　paper　proposes　a　fast　construction　method　using　a　large-scale　bridge　crack　dataset　based　on　a　residual　neural　network　(ResNet).　Firstly,　two　image　processing　tools　are　developed　to　solve　the　problems　of　incorrect　classification　of　images　due　to　the　lack　of　relative　spatial　information　and　the　omission　of　subtle　image　features.　Environmental　samples　are　introduced　without　losing　recognition　accuracy　by　combining　the　engineering　requirements.　Further,　the　detailed　process　of　building　a　large　dataset　is　given　based　on　the　ResNet50　model　and　transfer　learning　method.　Finally,　the　validity　and　feasibility　of　the　proposed　method　are　analyzed　through　the　construction　of　a　large-scale　bridge　crack　image　dataset,　and　the　distribution　pattern　of　image　data　behind　the　proposed　method　is　revealed.　By　comparing　the　differences　in　time　and　accuracy　of　traditional　dataset　construction　strategies,　the　superiority　of　the　proposed　dataset　construction　method　is　verified.　©　2025　IOP　Publishing　Ltd.　All　rights,　including　for　text　and　data　mining,　AI　training,　and　similar　technologies,　are　reserved.