Many　of　the　detection　methods　used　in　robotic　grasping　tasks　do　not　fully　consider　the　multiscale　information　of　graspable　objects,　thus　reducing　the　accuracy　of　grasp　detection　in　complex　scenarios.　To　overcome　this　problem,　we　propose　a　new　hybrid　grasp-detection　network　model　with　a　skip-connected　encoder-decoder　structure　called　HBGNet.　To　realize　the　complete　fusion　of　different　feature　information　dimensions,　a　multiscale　skip　connection　mechanism　is　designed　to　add　the　features　of　various　convolutional　neural　network　(CNN)　blocks　in　the　encoder　to　the　corresponding　concat　blocks　in　the　decoder.　The　HBGNet　encoder　is　designed　as　a　multiscale　hybrid　(MCT)　encoder,　by　combining　multiple　CNN　blocks　and　transformer　layers　to　acquire　high-　and　low-level　features　simultaneously.　The　proposed　HBGNet　was　trained　and　tested　on　the　publicly　available　Cornell　and　Jacquard　grasping　datasets　with　accuracies　of　99.75%　and　97.36%,　respectively.　The　performance　of　HBGNet　was　demonstrated　via　comparison　and　ablation　experiments.　A　real-world　robot　grasp　experiment　was　performed　on　the　AUBO-i5　robotic　platform　to　verify　the　generalization　ability　of　HBGNet,　with　an　average　grasp　success　rate　of　97.1%.　Experimental　results　indicate　that　HBGNet　can　fully　acquire　multiscale　information　of　grasped　objects　using　hybrid　networks　and　effectively　complete　grasp　detection　tasks　in　cluttered　scenes　in　a　generalizable　manner.