This　paper　presents　a　light-weight　Hierarchical　Fusion　Convolutional　Neural　Network　(HF-CNN)　which　can　be　used　for　grasping　detection.　The　network　mainly　employs　residual　structures,　atrous　spatial　pyramid　pooling　(ASPP)　and　codingdecoding　based　feature　fusion.　Compared　with　the　usual　grasping　detection,　the　network　in　this　paper　greatly　improves　the　robustness　and　generalizability　on　detecting　tasks　by　extensively　extracting　feature　information　of　the　images.　In　our　test　with　the　Cornell　University　dataset,　we　achieve　85%　accuracy　when　detecting　the　unknown　objects.　©　2021　Institute　of　Physics　Publishing.　All　rights　reserved.