In　construction,　concrete　compatibility　is　an　important　comprehensive　index　to　ensure　the　construction,　and　the　concrete　slump　is　an　important　criterion　to　judge　concrete　compatibility　in　the　actual　construction　process.　In　this　study,　we　propose　to　extract　new　data　sets　from　concrete　mixing　video　sequences　and　correlate　the　image　features　characterized　in　the　concrete　mixing　and　transportation　process　with　the　concrete　performance　features,　starting　from　the　concrete　transportation　process　[1].　First,　the　UNet　network　model　for　semantic　segmentation　is　used　to　identify　and　locate　the　concrete　regions,　and　the　localized　concrete　regions　are　zoomed　in　using　interpolation;　then　the　ResNet　image　classification　network　is　used　to　determine　the　slump　category　of　the　processed　concrete　regions;　finally,　the　results　of　the　semantic　segmentation　network　and　the　image　classification　network　are　fused　to　obtain　the　final　concrete　slump　detection　results.　The　experimental　results　demonstrate　that　the　proposed　method　can　guarantee　real-time　concrete　slump　detection　with　improved　accuracy.　©　Published　under　licence　by　IOP　Publishing　Ltd.