Pavement　crack　detection　is　of　great　significance　for　road　maintenance.　However,　the　complexity　of　road　surfaces　and　the　irregularity　of　cracks　make　it　difficult　to　accurately　detect　crack　regions.　We　propose　a　crack　detection　method　based　on　structural　features　for　the　patch-wise　crack　detection.　The　novelty　of　this　method　lies　on　the　fusion　of　the　local　patches　in　a　multi-staged　strategy.　Deep　supervision　learning　is　further　used　to　learn　these　features　at　each　stage.　The　fusion　features　model　the　structural　relevance　among　cracks.　The　experimental　results　prove　the　effectiveness　of　our　method　on　the　dataset　collected　from　the　industrial　environments.　Among　these　state-of-the-art　methods　we　compared,　our　model　achieved　the　best　experimental　results　with　an　AP　86.97%.