Bridges　play　an　important　role　in　modern　transportation　systems　and　road　networks,　and　hence　it　is　essential　to　use　various　models　based　on　visual　inspection　to　detect　and　prevent　the　damages　on　the　surface　of　bridge　structure.　However,　due　to　the　limitation　of　traditional　models　or　lack　of　modelling　data,　bridge　damages　are　often　difficult　to　be　accurately　detected.　This　paper　proposed　a　novel　deep　learning　model　called　Bridge　Detection　Transformers　(BR-DETR)　based　on　Detection　Transformers　(DETR).　Through　analysis　of　existing　bridge　damage　instances,　we　used　a　copy-paste　data　augmentation　method　to　create　new　samples　and　significantly　increased　the　sample　size.　Convolution　was　replaced　by　Deformable　Conv2D,　which　introduces　two-dimensional　offsets　to　the　regular　grid　sampling　positions　of　standard　convolution.　Convolutional　Project　Attention　was　also　added　after　the　self-attention　layer,　which　enabled　additional　modeling　of　local　spatial　context.　In　each　encoder　and　decoder　layer,　Locally-enhanced　Feed-Forward　(LeFF)　was　used　to　replace　the　Feedforward　to　promote　the　correlation　between　adjacent　tokens　in　the　spatial　dimension.　The　BR-DETR　model　outperformed　the　DETR　model　in　detection　performance　with　increased　mAP　and　recall　on　the　augmented　highway　bridge　damage　dataset　and　on　the　augmented　Shandong　bridge　damage　dataset.