To　meet　the　demand　for　high-precision　recognition　and　real-time　processing　when　using　intelligent　detection　methods　in　daily　concrete　bridge　crack　detection　tasks,　a　high-precision　lightweight　crack　image　recognition　method　based　on　knowledge　distillation　is　proposed.　Through　semisupervised　learning,　the　information　embedded　in　unlabeled　data　is　used　to　train　a　lightweight　network　to　obtain　high-precision　crack　recognition　results.　First,　by　comparing　the　recognition　accuracy　of　lightweight　network　under　different　training　strategies,　the　effectiveness　of　the　knowledge　distillation　method　in　improving　the　accuracy　of　lightweight　network　is　verified.　Second,　a　label-free　knowledge　distillation　method　for　engineering　detection　is　proposed.　Finally,　using　MobileNetv3　as　the　student　model,　under　the　self-built　three-classification　dataset　of　nearly　700,000　images,　the　accuracy　reached　99.84%　through　the　proposed　knowledge　distillation　method,　which　is　very　close　to　the　teacher　model　ResNeXt101　accuracy　(-0.08%),　and　MobileNetv3　compared　with　ResNeXt101　its　model　inference　speed　increased　by　more　than　13　times.　At　the　same　time,　the　MobileNetv3　model　improves　the　inference　speed　by　more　than　13　times　and　reduces　the　number　of　model　parameters　by　more　than　35　times　compared　to　the　ResNeXt101　model.　The　research　results　fully　demonstrate　that　under　the　premise　of　having　a　large　amount　of　unlabeled　data,　using　the　proposed　knowledge　distillation　method,　the　recognition　accuracy　of　the　trained　lightweight　model　has　a　significant　advantage　over　the　existing　lightweight　network　training　strategies.