A　recurrent　neural　network　(RNN)　called　CrackNet-R　is　proposed　in　the　article　for　fully　automated　pixel-level　crack　detection　on　three-dimensional　(3D)　asphalt　pavement　surfaces.　In　the　article,　a　new　recurrent　unit,　gated　recurrent　multilayer　perceptron　(GRMLP),　is　proposed　to　recursively　update　the　internal　memory　of　CrackNet-R.　Unlike　the　widely　used　long　short-term　memory　(LSTM)　and　gated　recurrent　unit　(GRU),　GRMLP　is　intended　for　deeper　abstractions　on　the　inputs　and　hidden　states　by　conducting　multilayer　nonlinear　transforms　at　gating　units.　CrackNet-R　implements　a　two-phase　sequence　processing:　sequence　generation　and　sequence　modeling.　Sequence　generation　is　specifically　developed　in　the　study　to　find　the　best　local　paths　that　are　most　likely　to　form　crack　patterns.　Sequence　modeling　predicts　timely　probabilities　of　the　input　sequence　being　a　crack　pattern.　In　terms　of　sequence　modeling,　GRMLP　slightly　outperforms　LSTM　and　GRU　by　using　only　one　more　nonlinear　layer　at　each　gate.　In　addition　to　sequence　processing,　an　output　layer　is　proposed　to　produce　pixel　probabilities　based　on　timely　probabilities　predicted　for　sequences.　The　proposed　output　layer　is　critical　for　pixel-perfect　accuracy,　as　it　accomplishes　the　transition　from　sequence-level　learning　to　pixel-level　learning.　Using　3,000　diverse　3D　images,　the　training　of　CrackNet-R　is　completed　through　optimizing　sequence　modeling,　sequence　generation,　and　the　output　layer　serially.　The　experiment　using　500　testing　pavement　images　shows　that　CrackNet-R　can　achieve　high　Precision　(88.89%),　Recall　(95.00%),　and　F-measure　(91.84%)　simultaneously.　Compared　with　the　original　CrackNet,　CrackNet-R　is　about　four　times　faster　and　introduces　tangible　improvements　in　detection　accuracy.