Optical　music　recognition　(OMR)　is　an　area　in　music　information　retrieval.　Music　object　detection　is　a　key　part　of　the　OMR　pipeline.　Notes　are　used　to　record　pitch　and　duration　and　have　semantic　information.　Therefore,　note　recognition　is　the　core　and　key　aspect　of　music　score　recognition.　This　paper　proposes　an　end-to-end　detection　model　based　on　a　deep　convolutional　neural　network　and　feature　fusion.　This　model　is　able　to　directly　process　the　entire　image　and　then　output　the　symbol　categories　and　the　pitch　and　duration　of　notes.　We　show　a　state-of-the-art　recognition　model　for　general　music　symbols　which　can　get　0.92　duration　accurary　and　0.96　pitch　accuracy　.