In　order　to　enhance　the　efficiency　and　accuracy　of　bridge　crack　detection,　this　paper　proposes　an　efficient　bridge　crack　identification　method　based　on　transfer　learning,　attention　mechanism　and　lightweight　convolutional　neural　network.　First,　based　on　the　characteristics　of　small　proportion　of　cracks　and　prominent　edges　in　bridge　images,　a　new　attention　mechanism　CAM　suitable　for　identifying　bridge　cracks　is　proposed　and　embedded　into　the　lightweight　network　EfficientNetv2　to　establish　the　CAM-EfficientNetv2　model.　Second,　for　the　problem　of　poor　training　effect　and　network　overfitting　due　to　the　small　sample　dataset,　the　transfer　learning　method　is　introduced　during　network　training.　Finally,　a　bridge　crack　image　dataset　is　constructed　and　the　performance　of　the　proposed　method　is　analyzed　in　detail　to　verify　the　feasibility　for　crack　identification.　The　experimental　results　demonstrate　that　the　proposed　method　can　achieve　the　overall　precision,　recall　and　F1-score　of　96.46%,　96.59%　and　96.64%,　respectively.　For　different　models　constructed　by　other　attention　mechanisms　embedded　in　EfficientNetv2　network,　our　method　performs　the　best.　Furthermore,　it　has　significant　advantages　in　terms　of　convergence　speed　and　running　speed,　while　also　ensuring　high　accuracy.　Our　research　outcome　fully　shows　that　this　method　can　solve　the　problems　of　poor　training　performance,　low　detection　accuracy,　and　slow　detection　speed　caused　by　small　sample　datasets　of　existing　detection　networks　in　bridge　crack　recognition.　©　2025　IOP　Publishing　Ltd.　All　rights,　including　for　text　and　data　mining,　AI　training,　and　similar　technologies,　are　reserved.