The　problem　of　object　detection　is　a　critical　issue　in　the　field　of　image　processing,　and　the　quality　of　labels　can　significantly　affect　the　performance　of　detectors.　In　recent　years,　many　deep　learning　frameworks　have　achieved　good　detection　results.　However,　in　many　practical　scenarios　of　object　detection,　collecting　complete　annotation　is　a　time-consuming　and　costly　process.　Therefore,　how　to　use　partially　labeled　samples　to　solve　the　object　detection　problem　has　attracted　the　attention　of　many　researchers.　We　proposed　a　new　joint　training　framework　based　on　PU　(Positive-Unlabeled)　learning　and　active　learning　to　solve　the　above　problem,　named　the　PU-AL　learning　method.　This　framework　is　based　on　the　Faster　RCNN　two-stage　detector,　and　we　introduce　the　PU　learning　method　to　solve　the　problem　of　incomplete　annotations,　adapting　the　traditional　semi-supervised　learning　problem　to　a　PU　learning　problem.　We　also　introduce　the　loss　branch　prediction　method　based　on　Active　Learning　to　improve　the　selective　labeling　method　of　sample　selection.　Experiment　results　demonstrate　that　the　proposed　framework　outperforms　the　baseline　methods　in　varying　degrees　of　missing　annotation.　　©　2023　IEEE.