In　large-scale　disaster　events,　the　planning　of　optimal　rescue　routes　depends　on　the　object　detection　ability　at　the　disaster　scene,　with　one　of　the　main　challenges　being　the　presence　of　dense　and　occluded　objects.　Existing　methods,　which　are　typically　based　on　the　RGB　modality,　struggle　to　distinguish　targets　with　similar　colors　and　textures　in　crowded　environments　and　are　unable　to　identify　obscured　objects.　To　this　end,　we　first　construct　two　multimodal　dense　and　occlusion　vehicle　detection　datasets　for　large-scale　events,　utilizing　RGB　and　height　map　modalities.　Based　on　these　datasets,　we　propose　a　multimodal　collaboration　network　(MuDet)　for　dense　and　occluded　vehicle　detection,　MuDet　for　short.　MuDet　hierarchically　enhances　the　completeness　of　discriminable　information　within　and　across　modalities　and　differentiates　between　simple　and　complex　samples.　MuDet　includes　three　main　modules:　Unimodal　Feature　Hierarchical　Enhancement　(Uni-Enh),　Multimodal　Cross　Learning　(Mul-Lea),　and　Hard-easy　Discriminative　(He-Dis)　Pattern.　Uni-Enh　and　Mul-Lea　enhance　the　features　within　each　modality　and　facilitate　the　cross-integration　of　features　from　two　heterogeneous　modalities.　He-Dis　effectively　separates　densely　occluded　vehicle　targets　with　significant　intra-class　differences　and　minimal　inter-class　differences　by　defining　and　thresholding　confidence　values,　thereby　suppressing　the　complex　background.　Experimental　results　on　two　re-labeled　multimodal　benchmark　datasets,　the　4K　Stereo　Aerial　Imagery　of　a　Large　Camping　Site　(4K-SAI-LCS)　dataset,　and　the　ISPRS　Potsdam　dataset,　demonstrate　the　robustness　and　generalization　of　the　MuDet.